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    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Bayesian Linear Regression with Metropolis-Hastings  \n",
    "\n",
    "### Michael J. Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*"
   ]
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    "#### Bayesian Updating\n",
    "\n",
    "The frequentist formulation of the linear regression model is: \n",
    "\n",
    "\\begin{equation}\n",
    "y = b_1 \\times x + b_0 + \\sigma\n",
    "\\end{equation}\n",
    "\n",
    "where $x$ is the predictor feature, $b_1$ is the slope parameter, $b_0$ is the intercept parameter and $\\sigma$ is the error or noise. There is an analytical form for the ordinary least squares solution to fit the available data while minimizing the L2 norm of the data error vector.\n",
    "\n",
    "For the Bayesian formulation of linear regression is we pose the model as a prediction of the distribution of the response, $Y$, now a random variable:\n",
    "\n",
    "\\begin{equation}\n",
    "Y \\sim N(\\beta^{T}X, \\sigma^{2} I)\n",
    "\\end{equation}\n",
    "\n",
    "We estimate the model parameter distributions through Bayesian updating for infering the model parameters from a prior and likelihood from training data.\n",
    "\n",
    "\\begin{equation}\n",
    "p(\\beta | y, X) = \\frac{p(y,X| \\beta) p(\\beta)}{p(y,X)}\n",
    "\\end{equation}\n",
    "\n",
    "In general for continuous features we are not able to directly calculate the posterior and we must use a sampling method, such as Markov chain Monte Carlo (McMC) to sample the posterior.\n",
    "\n",
    "For a complete lecture with linked Python workflows check out:\n",
    "\n",
    "* [Bayesian Linear Regression Lecture](https://www.youtube.com/watch?v=LzZ5b3wdZQk&list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf&index=33)\n",
    "\n",
    "* [Bayesian Probability Lecture](https://www.youtube.com/watch?v=Ppwfr8H177M&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=6)\n",
    "\n",
    "Here's a McMC Metropolis Hastings workflow and more details:\n",
    "\n",
    "* [Bayesian Linear Regression Workflow](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_BayesianRegression.ipynb)\n",
    "\n",
    "#### Bayesian Linear Regression with the Metropolis-Hastings Sampler\n",
    "\n",
    "Here's the basic steps of the Metropolis-Hastings Sampler: \n",
    "\n",
    "For $\\ell = 1, \\ldots, L$:\n",
    "\n",
    "1. Assign random values for the the initial sample of model parameters, $\\beta(\\ell = 1) = b_1(\\ell = 1)$, $b_0(\\ell = 1)$ and $\\sigma^2(\\ell = 1)$. \n",
    "2. Propose new model parameters based on a proposal function, $\\beta^{\\prime} = b_1$, $b_0$ and $\\sigma^2$. \n",
    "3. Calculate probability of acceptance of the new proposal, as the ratio of the posterior probability of the new model parameters given the data to the previous model parameters given the data multiplied by the probability of the old step given the new step divided by the probability of the new step given the old. \n",
    "\n",
    "\\begin{equation}\n",
    "P(\\beta \\rightarrow \\beta^{\\prime}) = min\\left(\\frac{p(\\beta^{\\prime}|y,X) }{ p(\\beta | y,X)} \\cdot \\frac{p(\\beta^{\\prime}|\\beta) }{ p(\\beta | \\beta^{\\prime})},1\\right)\n",
    "\\end{equation}\n",
    "\n",
    "4. Apply Monte Carlo simulation to conditionally accept the proposal, if accepted, $\\ell = \\ell + 1$, and sample $\\beta(\\ell) = \\beta^{\\prime}$\n",
    "5. Go to step 2.\n",
    "\n",
    "Let's talk about this system. First the left hand side:\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{p(\\beta^{\\prime}|y,X) }{ p(\\beta | y,X)}\n",
    "\\end{equation}\n",
    "\n",
    "We are calculating the ratio of the posterior probability (likelihood times prior) of the model parameters given the data and prior model for proposed sample over the current sample. \n",
    "\n",
    "* As you will see below it is quite practical to calculate this ratio.\n",
    "* If the proposed sampled is more likely than the current sample, we will have a value greater than 1.0, it will truncate to 1.0 and we accept the proposed sample.\n",
    "* If the proposed sample is less likely than the current sample, we will have a value less than 1.0, then we will use Monte Carlo sampling to randomly choice the proposed sample with this probability of acceptance.\n",
    "\n",
    "This proceedure allows us to walk through the model parameter space and sample the parameters with the current rates, after the burn-in chain.\n",
    "\n",
    "Now, what about this part of the equation?\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{p(\\beta^{\\prime}|\\beta) }{ p(\\beta | \\beta^{\\prime})}\n",
    "\\end{equation}\n",
    "\n",
    "There is a problem with this procedure if we use asymmetric probability distributions for the model parameters! \n",
    "\n",
    "* E.g. for example, if we use a positively skewed distribution (e.g., log normal) then we are more likely to step to larger values due to this distribution, and not due to the prior nor the likelihood.\n",
    "\n",
    "* This term removes this bias, so that we have fair samples!\n",
    "\n",
    "You will see below that we remove this issue by assuming symmetric model parameter distributions, even though many use  na assymetric gamma distribution for sigma, given it cannot have negative values.\n",
    "\n",
    "* My goal is the simplest possible demonstration.\n",
    "\n",
    "#### Our Simplified Demonstration Metropolis Sampling\n",
    "\n",
    "Let's further specify this workflow for our simple demonstration. \n",
    "\n",
    "1. I have assumed a Gaussian, symmetric distribution for all model parameters as a result this relationship holds for all possible model parameter, current and proposed.\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{p(\\beta^{\\prime}|\\beta) }{ p(\\beta | \\beta^{\\prime})} = 1.0\n",
    "\\end{equation}\n",
    "\n",
    "    So we now have this simplified probability of proposal acceptance, note this is know as Metropolis Sampling.\n",
    "    \n",
    "\\begin{equation}\n",
    "P(\\beta \\rightarrow \\beta^{\\prime}) = min \\left( \\frac{p(\\beta^{\\prime}|y,X) }{ p(\\beta | y,X)},1 \\right) \n",
    "\\end{equation}\n",
    "\n",
    "2. Now, let's substitute our Bayesian formulation for our Bayesian linear regression model.\n",
    "\n",
    "\\begin{equation}\n",
    "p(\\beta^{\\prime} | y, X) = \\frac{p(y,X| \\beta^{\\prime}) p(\\beta^{\\prime})}{p(y,X)} \\quad \\text{      and        } \\quad p(\\beta | y, X) = \\frac{p(y,X| \\beta) p(\\beta)}{p(y,X)}\n",
    "\\end{equation}\n",
    "\n",
    "    If we substitute these into our probability of acceptance above we get this.\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "P(\\beta \\rightarrow \\beta^{\\prime}) = min \\left( \\frac{p(\\beta^{\\prime}|y,X) }{ p(\\beta | y,X)},1 \\right) = min \\left( \\frac{ \\left( \\frac{p(y,X| \\beta_{new}) p(\\beta_{new}) } {p(y,X)} \\right) }{ \\left( \\frac{ p(y,X| \\beta) p(\\beta)}{p(y,X)} \\right) },1 \\right)\n",
    "\\end{equation}\n",
    "\n",
    "    Note that the evidence terms cancel out.\n",
    "\n",
    "\\begin{equation}\n",
    "P(\\beta \\rightarrow \\beta^{\\prime}) = min \\left( \\frac{ p(y,X| \\beta_{new}) p(\\beta_{new}) }{ p(y,X| \\beta) p(\\beta)},1 \\right)\n",
    "\\end{equation}\n",
    "\n",
    "    Since we are working with a likelihood ratio, we can work with densities instead of probabilities.\n",
    "\n",
    "\\begin{equation}\n",
    "P(\\beta \\rightarrow \\beta^{\\prime}) = min \\left( \\frac{ f(y,X| \\beta_{new}) f(\\beta_{new}) }{ f(y,X| \\beta) f(\\beta) } ,1 \\right)\n",
    "\\end{equation}\n",
    "\n",
    "3. Finally for improved solution stability we can calculate the natural log ratio:\n",
    "\n",
    "\\begin{equation}\n",
    "ln(P(\\beta \\rightarrow \\beta^{\\prime})) = min \\left( ln \\left[ \\frac{ f(y,X| \\beta_{new}) f(\\beta_{new}) }{ f(y,X| \\beta) f(\\beta) } \\right],0 \\right) = min \\left( \\left[ln(f(y,X| \\beta_{new})) + ln(f(\\beta_{new})) \\right] - \\left[ ln(f(y,X| \\beta)) + ln(f(\\beta)) \\right],0 \\right)\n",
    "\\end{equation}\n",
    "\n",
    "4. We calculate probability of proposal acceptance, as exponentiation of the above. \n",
    "\n",
    "5. How do we calculate the likelihood density? If we assume independence between all of the data we can take the product sum of the probabilities (densities) of all the response values given the predictor and model parameter sample! Given, the Gaussian assumption for the response feature, we can calculate the densities for each data from the Gaussian PDF.\n",
    "\n",
    "\\begin{equation}\n",
    "f_{y,X | \\beta}(y) \\sim N [ b_1 \\cdot X + b_0, \\sigma ]\n",
    "\\end{equation}\n",
    "\n",
    "    and under the assumption of indepedence we can take the produce sum over all training data.\n",
    "\n",
    "\\begin{equation}\n",
    "f(y,X| \\beta) = \\prod_{\\alpha = 1}^{n} f_{y,X | \\beta}(y_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "Note, this workflow was developed with assistance from Fortunato Nucera's Medium Article [Mastering Bayesian Linear Regression from Scratch: A Metropolis-Hastings Implementation in Python](https://medium.com/@tinonucera/bayesian-linear-regression-from-scratch-a-metropolis-hastings-implementation-63526857f191). I highly recommend this accessible description and demonstration. Thank you, Fortunato.\n",
    "\n",
    "#### Load and Configure the Required Libraries\n",
    "\n",
    "The following code loads the required libraries and sets a plotting default."
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   "source": [
    "import numpy as np                                          # arrays and matrix math\n",
    "import pandas as pd                                         # DataFrames\n",
    "import matplotlib.pyplot as plt                             # plotting\n",
    "from matplotlib.patches import Rectangle                    # build a custom legend\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "import seaborn as sns                                   # plot PDF\n",
    "import scipy.stats as stats                                 # parametric distributions\n",
    "from sklearn.linear_model import LinearRegression           # frequentist model for comparison\n",
    "from IPython.display import display_markdown                # add markdown to code outputs \n",
    "plt.rc('axes', axisbelow=True)                              # set axes and grids in the background for all plots\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')                           # ignore warnings\n",
    "seed = 73073                                                # random number seed"
   ]
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    "#### Declare Functions\n",
    "\n",
    "The following functions include:\n",
    "    \n",
    "* **next_proposal** - propose a next model parameters from previous previous model parameters, this is the proposal method, parameterized by step standarrd deviation and Gaussian distribution.\n",
    "\n",
    "* **likelihood_density** - calculate the product of all the densities for all the data given the model parameters. Since we are working with the log densities, we sum over all the data.\n",
    "\n",
    "* **prior_density** - calculate the product of the densities for all the model parameters given the prior model. Since we are working with the log densities, we sum over all the model parameters. This is an assumption of independence.\n",
    "\n",
    "* **add_grid** - improved grid for the plotting."
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    "def next_proposal(prev_theta, step_stdev = 0.5):            # assuming a Gaussian distribution centered on previous theta and step stdev  \n",
    "    out_theta = stats.multivariate_normal(mean=prev_theta,cov=np.eye(3)*step_stdev**2).rvs(1)\n",
    "    return out_theta\n",
    "\n",
    "def likelihood_density(x,y,theta):                          # likelihood - probability (density) of the data given the model\n",
    "    density = np.sum(stats.norm.logpdf(y, loc=theta[0]*x+theta[1],scale=theta[2])) # assume independence, sum is product in log space\n",
    "    return density\n",
    "\n",
    "def prior_density(theta,prior):                             # prior - probability (density) of the model parameters given the prior \n",
    "    mean = np.array([prior[0,0],prior[1,0],prior[2,0]]); cov = np.zeros([3,3]); cov[0,0] = prior[0,1]; cov[1,1] = prior[1,1]; cov[2,2] = prior[2,1]\n",
    "    prior_out = stats.multivariate_normal.logpdf(theta,mean=mean,cov=cov,allow_singular=True)\n",
    "    return prior_out\n",
    "\n",
    "def add_grid():\n",
    "    plt.gca().grid(True, which='major',linewidth = 1.0); plt.gca().grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks"
   ]
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    "#### Build a Dataset with Known Truth Model Parameters for Linear Regression\n",
    "\n",
    "Let's build a simple dataset with known linear regresin parameters, $b_1$ is the slope parameter, $b_0$ is the intercept parameter and $\\sigma$ is the error or noise."
   ]
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   "cell_type": "code",
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   "id": "55a1c55a",
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    {
     "data": {
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dUr8mhf9yJCIlU5EtUoP99ttvTJ06lfDwcMaOHQvYixzDMIotQ/fMM8+Qn59fpK3gnFNH6AoKpVNjFF7B5HS8vLzw9/cvUnDt37+/xNVFyiIkJIQuXbrw5ptv8tBDDzmmjBw9epR33nmnQjHN1r9/f+bNm0edOnUcRWNJSvraGobB008/7ZS8vLy8SE1N5cILL+T+++/nww8/5OKLL6ZWrVrs3LmTO++8s9TX+vv707lzZ9566y0efvhhx5SRzMzMEld/KUlwcDC9evVi69attGvXrsi0k9OpVasW119/PXv37mXixIn88ssvXHjhhUXOOffcc7nzzjv59NNP+b//+79SY/Xu3RuLxcILL7zApEmTHO1vvPEGWVlZjoc8nWHKlCkEBQU5VvIpSdeuXQkKCuKFF14oct6ePXv47LPPuP766wFo1aoV55xzDitXrmTy5MmO76Vff/2Vr776qshfs/r37+94QDgmJsZJdydSvanIFqkhvv32W8e8ygMHDvDll1+ybNkyfHx8WLVqlWOE7qyzzqJ79+489NBDnH322TRt2pR169bx7LPPUqtWrSIx27RpA8BTTz1FWFgYgYGBNGvWjAsuuIDzzz+fadOmYRgGERERvPPOO3z88cdlyrV///68+eabjB8/nuuvv57ff/+dBx98kHPOOYcff/yxQvf/4IMPcuWVV3LFFVcwZcoU8vPzSU5OJiQkpMwj9Pn5+bz++uvF2kNCQujXr1+F8iowceJE3njjDbp3786kSZNo164dNpuN3377jY8++ogpU6YQExPDFVdcgb+/PzfddBMJCQnk5uaydOlSDh48WKnrn06LFi24/fbbefzxx1m/fj2XXHIJS5YsYcSIEfz7779cf/311KtXj7/++ovt27fz119/sXTpUsC+Q+HVV19N3759mTBhAvn5+Tz00EOEhoaW+eu+ePFiLrnkEi699FLuuOMOmjZtytGjR9m1axfvvPOOY870gAEDaNOmDZ07d6Zu3br8+uuvLFq0iCZNmtCiRQsOHz5Mr169GDp0KBdccAFhYWGkp6c7VkApTcFofmJiIkeOHOHiiy92rC7SsWNHhg0bVvkvcin69OlDnz59TntOrVq1eOCBB7j33nsZPnw4N910E//88w+zZs0iMDDQsfqKt7c3Dz74ILfddhuDBg1izJgxHDp0iKSkpGLTRYYMGcKLL77IVVddxYQJE+jSpQt+fn7s2bOHtWvXMnDgQAYNGuS0+xapFlz3zKWIVIWCFS8K/vn7+xv16tUzevToYcybN884cOBAsdfs2bPHuO6664zatWsbYWFhxpVXXml8++23RpMmTYwRI0YUOXfRokVGs2bNDB8fHwMwli1bZhiGYezcudO44oorjLCwMKN27drGDTfcYPz2228GYMycOfOMeS9YsMBo2rSpERAQYLRu3dp4+umnHatfFMbJ1RhOVVKuq1evNtq1a2f4+/sb5557rrFgwYISY5ZkxIgRRb6Ohf8VrMpQ2uoiUVFRJcYrvJqDYRhGZmamcf/99xutWrUy/P39jfDwcKNt27bGpEmTjP379zvOe+edd4z27dsbgYGBRsOGDY177rnH+OCDD4qt9FLatUtT8LX466+/in3uzz//NEJDQ41evXo52tatW2dcffXVRkREhOHn52c0bNjQuPrqq43XXnutyGtXrVpltG3btsjX/e677zZq165d5LzS+tIwDGP37t3GqFGjjIYNGxp+fn5G3bp1jW7duhlz5sxxnPPII48Y3bp1M84++2zHtUaPHm388ssvhmEYRm5urjFu3DijXbt2xllnnWUEBQUZrVq1MmbOnGlkZWU54pTUNzk5OUZiYqLRpEkTw8/PzzjnnHOMO+64wzh48GCR85o0aWJcffXVxfIv6wocp/saFDh1dZECzzzzjOP7Ozw83Bg4cKDx3XffFXv9M888Y7Ro0cLw9/c3WrZsaTz33HMl3vOJEyeMhx9+2PG9FhoaalxwwQXG2LFjjR9//LHc9yZS03gZxinLCoiIiDjRiRMn6NChAw0bNuSjjz5ydToiIk6h6SIiIuJUo0eP5oorruCcc85h//79PPHEE3z//fdFdmIUEaluVGSLiIhTHT16lKlTp/LXX3/h5+dHp06deP/997n88stdnZqIiNNouoiIiIiIiMlcuhnNF198wYABA4iMjMTLy4u33nrL8bkTJ06QmJhI27ZtCQkJITIykuHDh/PHH38UiXHs2DHuuusuzj77bEJCQrjmmmvYs2dPFd+JiIiIiMh/XFpkZ2Vl0b59ex577LFin8vOzmbLli088MADbNmyhTfffJOMjAyuueaaIudNnDiRVatW8fLLL7N+/XoyMzPp379/sfV8RURERESqittMF/Hy8mLVqlVce+21pZ6Tnp5Oly5d+PXXXzn33HM5fPgwdevWJS0tjRtvvBGw727VuHFj3n//ffr27VtF2YuIiIiI/MejHnw8fPgwXl5ejg0xNm/ezIkTJ4os1B8ZGUmbNm346quvSi2yjx07dsbtmW02G//++y916tSp0LbEIiIiIuJZDMPg6NGjREZG4u1duQkfHlNk5+bmMm3aNIYOHcpZZ50F2LdZ9vf3p3bt2kXOrV+/Pvv37y811vz585k1a5ZT8xURERERz/T777/TqFGjSsXwiCL7xIkTDBkyBJvNxuOPP37G8w3DOO3o8/Tp05k8efJpYxw+fJhzzz0Xq9VabLvZisrJyQEgKCjIlHjOiumsuM6ImZmZyfbt22nfvj2hoaGmxfWU+/ek/ndGX3nS/XtKTPCcvqrp/a+ff57T/57ynnJWXE+JCfYB3JiYGMLCwiody+2L7BMnTjB48GB2797NZ5995hjFBmjQoAHHjx/n4MGDRUazDxw4QLdu3UqNGRAQQEBAQJmu36BBA84999yK30AhmZmZAKb+MHRGTGfFdUbMQ4cO8eOPPxIZGemYRmQGT7l/T+p/Z/SVJ92/p8QEz+mrmt7/+vnnOf3vKe8pZ8X1lJiFmTFV2KWri5xJQYH9448/8sknn1CnTp0in7/ooovw8/Pj448/drTt27ePb7/99rRFtoiIiIiIM7l0JDszM5Ndu3Y5jnfv3s22bduIiIggMjKS66+/ni1btvDuu++Sn5/vmGcdERGBv78/4eHhjB49milTplCnTh0iIiKYOnUqbdu21U5iIiIiIuIyLi2yN23aRK9evRzHBfOkR4wYQVJSEqtXrwagQ4cORV63du1aevbsCUBKSgq+vr4MHjyYnJwcevfuzfLly/Hx8amSexAREREROZVLi+yePXtyumW6y7KEd2BgIEuWLGHJkiVmpiYiIiIiUmFuPSdbRERERMQTqcgWERERETGZimwREREREZOpyBYRERERMZmKbBERERERk6nIFhERERExmYpsERERERGTqcgWERERETGZimwREREREZOpyBYRERERMZmKbBERERERk6nIFhERERExma+rE5DSDRs2jMOHD5/2HJvNBoC3t7m/LzkjrrNiZmdn8/TTT1cqbnh4OGlpaablJSIiIjWbimw3dvjwYVavXu3qNGqEa665xtUpiIiISDWi6SIiIiIiIiZTkS0iIiIiYjJNFzmD3NxcMjMzTYmVnZ1drvML5jCL89lstiL9XN6+KgtPiemsuDk5OY6Pvr7m/OjxpPv3lJjgOX1V0/vfGf0EnnP/ntT/nvKeclZcT4kJ9rrPLCqygdTUVFJTU4u05efnuygbERERESmQnp7Orl27aN68OdHR0a5Op8xUZAPx8fHEx8cXaTty5Ajh4eEEBgYSGhpq6vXKGs/sFUOkdN7e3iX2i9l970kxzY6bl5cHQFBQkMveU+4Q1xNielpf1dT+d2Y/gfvfvzNjmh3X095TzopbkZjTEhNJtlgcx4kJCSxITq5UzNMJDAw0LZaqOBERERFxO1arlWSLBQuQBViAZIsFq9Xq4szKRkW2iIiIiLidjIwMAOKB4JMfC7e7OxXZIiIiIuJ2WrZsCUAqkH3yY+F2d6ciW0RERETcTkxMDIkJCSQAIUAC9jnaMTExLs6sbPTgo4iIiIi4pQXJyQyKiyMjI4OWLVt6TIENKrJFRERExI3FxMR4VHFdQNNFRERERERMppFsEREREfEoBRvUtGvXzm1HuTWSLSIiIiIeY1piIpdddhm33347sbGxTEtMdHVKJVKRLSIiIiIewZM2qFGRLSIiIiIewZM2qFGRXc0kJibSr18/V6chIiIiYjpP2qBGRXY1s23bNtq3b+/qNJg/fz7R0dGEhYVRr149rr32Wn744Yczvu7o0aNMnDiRJk2aEBQURLdu3UhPT3d8Pi8vj/vvv59mzZoRFBTEeeedx+zZs7HZbM68HREREXEDnrRBjVYXqWa2b9/Orbfe6uo0WLduHfHx8URHR5OXl8d9991Hnz592LlzJyEhIaW+7rbbbuPbb78lLS2NyMhIXnjhBS6//HJ27txJw4YNSU5O5oknnmDFihVERUWxadMmbr31VsLDw5kwYUIV3qGIiIi4woLkZPpeeaXbry6iIrsa2b9/P3/++Sc2m43u3buzadMmLrzwQp599tkqH91es2ZNkeNly5ZRr149Nm/eTPfu3Ut8TU5ODm+88QZvv/2245ykpCTeeustli5dypw5c9iwYQMDBw7k6quvBqBp06asXLmSTZs2OfeGRERExG1ER0cTHR1NaGioq1MplaaLVCNbt24FYNGiRcybN49NmzYRFhbGkCFDyh1r3rx5hIaGnvbfl19+WeZ4hw8fBiAiIqLUc/Ly8sjPzycwMLBIe1BQEOvXrwfgkksu4dNPP3U84LB9+3bWr1/PVVddVd5bFBEREXEajWRXI9u2bSMwMJC33nqLyMhIAObOncvFF1/M/v37ueOOO/j888/p3bs3r7/++mljjRs3jsGDB5/2nIYNG5YpL8MwmDx5Mpdccglt2rQp9bywsDC6du3Kgw8+SOvWralfvz4rV67EarXSokULwP5g5+HDh7ngggvw8fEhPz+fuXPnctNNN5UpFxEREal5rFYrGRkZtGzZssqml6jIrka2bdvG4MGDHQU24Jj/bLPZuPvuuxk1ahQrVqw4Y6yIiIjTjjqXx5133smOHTsco9Gnk5aWxqhRo2jYsCE+Pj506tSJoUOHsmXLFgBeeeUVXnjhBV566SWioqLYtm0bEydOJDIykhEjRpiSr4iIiFQf0xITSbZYHMeJCQksSE52+nU1XaQa2bZtGx06dCjStmXLFho0aMA555xDr169CAsLK1Mss6aL3HXXXaxevZq1a9fSqFGjM55//vnns27dOjIzM/n999/5+uuvOXHiBM2aNQPgnnvuYdq0aQwZMoS2bdsybNgwJk2axPz588t0XyIiIlJzlHvzGhNXK9NIdjWRnZ3Nrl27yM/Pd7TZbDaWLFnCyJEj8fLyKle8yk4XMQyDu+66i1WrVvH55587iuSyCgkJISQkhIMHD/Lhhx9iOfkbaHZ2Nt7eRX839PHx0RJ+IiIiUkxJm9cknGwvNm1k1y5Chw417doqsquJ7du34+Pjw7Jly+jevTu1atXi3nvvJSsri3vvvbfc8So7XSQ+Pp6XXnqJt99+m7CwMPbv3w9AeHg4QUFBADz22GOsWrWKTz/91PG6Dz/8EMMwaNWqFbt27eKee+6hVatWjmUJBwwYwNy5czn33HOJiopi69atLFy4kFGjRlU4VxEREameCm9eE08pm9fYbPD445CYiG92tmnXVpF9Brm5uWRmZpoSK7ucHVee0dnt27fTsmVLZs6cyXXXXce///7LNddcw1dffVXmKSJmWrp0KQA9e/Ys0r5s2TJGjhwJwN9//81PP/1U5POHDx9m+vTp7Nmzh4iICK677jrmzp2Ln58fAEuWLOGBBx5g/PjxHDhwgMjISMaOHcuMGTMqla/NZivSz+Xtq7LwlJjOipuTk+P46Otrzo8eT7p/T4kJntNXNb3/ndFP4Dn370n97ynvKWfFdWXMqKgoJk2aREJKCgkn2yZPnkxUVBSZmZl4/fILAePH43tyCuyxLl3g669NyVFFNpCamkpqamqRtsLTLjzBuHHjGDduHAA33HCDi7OxTxc5k6SkJJKSkoq0DR48+LTTVMLCwli0aBGLFi2qZIYiIiJSE8yePZsBAwawa9cumjdvTnR0NNhs+D73HAH3349XVhZGSAjHZ8/m4FVXQevWplxXRTb2qQ3x8fFF2o4cOUJ4eDiBgYGmL3Re1ninzj2urL59+7JlyxaysrJo1KgRq1atsn+jCd7e3iX2izMWufeUmGbHzcvLA+zrnrvqPeUOcT0hpqf1VU3tf2f2E7j//TszptlxPe095ay4rozZq1cvevXqZT/45RcYPRo++8x+3KMHXs89R8B55xH422+m5aYiuwb58MMPXZ2CiIiISJWzWq1k/PADl+zcSbPUVMjMhKAgSE6G+HgweWATVGSLiIiISDU2LTGRlywWngEca51dcgksWwbNmzvtulonW0RERESqJevGjfxtsfAj0Ac4AUwErMnJTi2wQUW2iIiIiFRHe/bQ6PbbeQYIONmUBywGMk5Z3cwZVGSLiIiISPVhGPapIFFRNPzmG3KBtUA28NjJU4qsk+0kmpMtIiIiItXD3r1w++3w/vv245gYUtu0YeqzzzpOmZaYWHy3RydQkS0iIiIins0w4IUX4O674dAh8PeHBx+EyZOZ4uvLJWPGkJGRQcuWLaukwAYV2SIiIiJSRaxWq/nF7r59MHYsvPOO/bhzZ1i+HKKiHKfExMRUWXFdQHOyRURERGogq9VKWloaVqu1Sq43LTGR2NhYhg8fTmxsLNMSEysX0DDgxRftxfQ774CfH8ydCxs2FCmwXUVFtoiIiEgNY3rBewbp6ekkWyxYgCzAAiRbLBUu8L0OHCBw6FC45RY4eBA6dYLNm+Hee8HXPSZqqMgWERERqUGsVqupBW9Z7Nq1C4B4IPjkR4CMjIzyBTIMeOUVgqOj8X33Xfvo9YMPwsaN0LatmSlXmopsERERkRqkoLCtdMFbDs1PbvySin0pvdST7eVaSu/AARg8GIYMwevff8lv1w7S0+H+++3FtptRkS0iIiJSgxQUtpUqeMspOjqaxIQEEoAQIIFyLqX3+uv2edavvw6+vhyfPp2ctWuhfXun5VxZ7jFpRURERESqRExMjL3gtVhIONlWUPBmZmY67boLkpMZFBdXvtVF/v4b7rwTXnnFfty2LaxYwfEWLZyWp1lUZIuIiIjUMBUqeE1QrqX0Vq2CcePs00R8fGD6dHjgAfD3J33tWnbt2kW7du2qfGm+slKRLSIiIlIDuWLt6DL55x+46y5YudJ+HBVlX/e6c2fAPuqebLE4Tk9MSGBBcrILEj09zckWEREREffw9tv2onrlSvD2to9eb97sKLBdsTJKRanIFhERERHX+vdfGDYMrr0W/vwTWre2byozbx4EBDhOc8XKKBWlIltEREREXOfdd6FNG3jhBfvodUICbNkCXboUO9UVK6NUlIpsEREREal6hw7ByJEwYADs2wetWsH//R8kJ0NgYIkvcayMQgWXAqxCevCxmklMTGTHjh188MEHrk5FREREpGQffABjxsDeveDlBZMmwZw5EBR0xpcuSE6m75VXanURT5ebm2vampHZ2dnlOt9ms5X7Gtu2baNjx47lfp3Z5s+fz5tvvsn//vc/goKC6NatG8nJybRq1arIeY8//jgPPfQQ+/btIyoqikWLFnHppZeWGvfo0aM88MADrFq1igMHDtCxY0cWL15MdHS045wvvviChx56iM2bN7Nv3z5WrVrFtddee9p8bTZbkX4ub1+VhafEdFbcnJwcx0dfX3N+9HjS/XtKTPCcvqrp/e+MfgLPuX9P6n9PeU85K26RmIcPEzB9On5paQDYzj+f3KVLsXXtCvn5UMaaKyoqiqioKIKDg01d2zs3N9e0WJouAqSmpnLhhRcW+Ve4aPMk27dvp0OHDq5Og3Xr1hEfH8/GjRv5+OOPycvLo0+fPmRlZTnOeeWVV5g4cSL33XcfW7du5dJLL6Vfv3789ttvpca97bbb+Pjjj0lLS+Obb76hT58+XH755ezdu9dxTlZWFu3bt+exxx5z6j2KiIhI2fl8+inBMTH4paVheHlxPD6e7K++shfY1ZBGsoH4+Hji4+OLtB05coTw8HACAwMJDQ019XpljeftXb7fgfbv38+ff/6JzWaje/fubNq0iQsvvJBnn32W9lW87eiaNWuKHC9btox69eqxefNmunfvDsDChQsZPXo0t912GwCLFi3iww8/ZOnSpcyfP79YzJycHN544w3efvttR4ykpCTeeustli5dypw5cwDo168f/fr1K1e+3t7eJfaL2X3vSTHNjpuXlwdAUFCQy95T7hDXE2J6Wl/V1P53Zj+B+9+/M2OaHdfT3lOmxz1yhID77sNv+XL78fnn47VsGf6XXop/JUObff+BpcwFrwiNZFcjW7duBezF6rx589i0aRNhYWEMGTKk3LHmzZtHaGjoaf99+eWXZY53+PBhACIiIgA4fvw4mzdvpk+fPkXO69OnD1999VWJMfLy8sjPzy/2BggKCmL9+vXluT0RERGpCp9+SnBs7H8F9l13wfbtcJqpodWFRrKrkW3bthEYGMhbb71FZGQkAHPnzuXiiy9m//79bNq0iSlTpmCz2UhMTHSMIJdk3LhxDB48+LTXa9iwYZnyMgyDyZMnc8kll9CmTRsA/v77b/Lz86lfv36Rc+vXr8/+/ftLjBMWFkbXrl158MEHad26NfXr12flypVYrVZatGhRplxERESkCmRm2pfiW7oUb8DWtCney5ZBz56uzqzKqMiuRrZt28bgwYMdBTZASEgIYB85njx5MmvXruWss86iU6dOxMXFOUaWTxUREVHq58rrzjvvZMeOHSWONnt5eRU5NgyjWFthaWlpjBo1ioYNG+Lj40OnTp0YOnQoW7ZsMSVXERERqaS1a2HUKPjlFwCO3347x2fNIrRBA9fmVcU0XaQa2bZtW7GHHrds2UKDBg34/fffiYqKomHDhoSFhXHVVVfx4YcflhrLrOkid911F6tXr2bt2rU0atTI0X722Wfj4+NTbNT6wIEDxUa3Czv//PNZt24dmZmZ/P7773z99decOHGCZs2anTEXERERcaKsLPt0kMsusxfYTZrAp59y/JFHwElzx92ZRrKriezsbHbt2kV+fr6jzWazsWTJEkaOHMm+ffuKTO9o1KhRkRU5TlXZ6SKGYXDXXXexatUqPv/882JFsL+/PxdddBEff/wxgwYNcrR//PHHDBw48LTXBfsIfUhICAcPHuTDDz/EYrGc8TUiIiLiJF98AbfeCj//bD++/XZ4+GEICyvzsnzVjYrsamL79u34+PiwbNkyunfvTq1atbj33nvJysri3nvvLbbaBxSfqlFYZaeLxMfH89JLL/H2228TFhbmGLEODw8n6ORC85MnT2bYsGF07tyZrl278tRTT/Hbb78xbtw4R5zHHnuMVatW8emnnwLw4YcfYhgGrVq1YteuXdxzzz20atWKW2+91fGazMxMdu3a5TjevXs327ZtIyIignPPPbfC9yQiIiKnyM6Ge++FxYvtx40bw7PPwhVXuDYvN6Aiu5rYvn07LVu2ZObMmVx33XX8+++/XHPNNXz11VeEhYXRsGHDIiPXe/bsceoOSUuXLgWg5ykPOCxbtoyRI0cCcOONN/LPP/8we/Zs9u3bR5s2bXj//fdp0qSJ4/y///6bn376yXF8+PBhpk+fzp49e4iIiOC6665j7ty5+Pn5Oc7ZtGkTvXr1chxPnjwZgBEjRrC84OlmERERqZz16+2j1wUDW7fdBo88Amed5dq83ISK7Gpi3LhxjhHgG264odjnu3TpwrfffsvevXs566yzeP/995kxY4bT8jEMo0znjR8/nvHjx5f6+aSkJJKSkhzHgwcPPuM0lp49e5b5+iIiIlJOOTlw//2QkgKGAQ0b2kev+/Z1dWZuRUV2DeHr68sjjzxCr169sNlsJCQkUKdOHVenJSIiIp5kwwYYORIyMuzHt94KCxdCrVquzMotqciuQa655hquueYaV6chIiIiniY3F2bMsE8HsdkgMhKefhquusrVmbktFdkiIiIiUjqr1T56/b//2Y+HDbM/6Fi7tkvTcndaJ1tEREREijt2DKZPh27d7AV2gwawejU8/7wK7DLQSLaIiIiIFJWebh+93rnTfnzzzfDoo2DSbtA1gUayRURERMTu2DH8Z82Crl3tBXa9erBqFbzwggrsctJItoiIiIjAli0EDRuGT8Ho9ZAhsGQJnH22a/PyUBrJFhEREanJjh+3rxzSpQs+O3diO/tseP11WLlSBXYlaCRbREREpKbats0+93r7dgBOxMVx7OGHCW3WzKVpVQcayRYRERGpaU6cgFmzIDraXmDXqQOvvMKxFSugbl1XZ1ctuLTI/uKLLxgwYACRkZF4eXnx1ltvFfm8YRgkJSURGRlJUFAQPXv25LvvvityzrFjx7jrrrs4++yzCQkJ4ZprrmHPnj1VeBciIiIiHmTHDoiJgaQkyMuDuDj47jsYPNjVmVUrLi2ys7KyaN++PY899liJn7dYLCxcuJDHHnuM9PR0GjRowBVXXMHRo0cd50ycOJFVq1bx8ssvs379ejIzM+nfvz/5+flVdRsiIiIi7i8vD+bOhc6dYetW+2ohK1fa51/Xr+/q7Kodl87J7tevH/369Svxc4ZhsGjRIu677z7i4uIAWLFiBfXr1+ell15i7NixHD58mGeffZa0tDQuv/xyAF544QUaN27MJ598Qt++favsXkRERETc1rff2udeb95sPx44EJ54wr7BzGlYrVYyMjJo2bIlMTExzs+zGnHbBx93797N/v376dOnj6MtICCAHj168NVXXzF27Fg2b97MiRMnipwTGRlJmzZt+Oqrr0otso8dO8axY8dOe/0jR44AkJmZyaFDhyp/Q0B2djYAeXl5ZTrfZrOZcl05M5vNVqSfy9tXZeEpMZ0Vt+AvUIX/ElVZnnT/nhITPKevanr/O6OfwHPuv6wxN23axK5du2jevDmdO3c2LW55uPQ9lZdHwJIlBC5YgNfx49hq1SJnwQJODB4MXl5wSo1TOG5SUhKLFy92fG7ChAkkJSU5L1cXxwR73WcWty2y9+/fD0D9U/58Ub9+fX799VfHOf7+/tQ+ZWvP+vXrO15fkvnz5zNr1qwy5bF9+3Z+/PHH8qRumoJvIHG+7Oxs1q1b5+o0aoQtW7a4OgUpI/WVZ1A/le75FSt4c9Uqx3HcoEEMHzHCZflUdV+F/v47nR59lKCTdcz+zp3ZPn48uRER8MUXp31tRkYGixcvxgLEA6lAwuLFNGzYkJYtWzo9d1cxs/Zy2yK7gJeXV5FjwzCKtZ3qTOdMnz6dyZMnnzbGkSNHaNy4Me3btycyMrLsCZ9GQccFBweX6fynn37alOvKmQUHB9OjRw/HcXn7qiw8Jaaz4h49epQtW7bQqVMnwsLCTInpSffvKTHBc/qqpve/M/oJPOf+zxRz06ZNvLlqVdEicdUq7hg//rQj2p7SV6fNMz+fgNRUAufNw+vYMYyzziI7OZnAG28k5gw1VEHcffv2AfavXfDJjwlAWFhYkf9fVjrXCnLW+/+PP/4wLZbbFtkNTs4R2r9/P+ecc46j/cCBA47R7QYNGnD8+HEOHjxYZDT7wIEDdOvWrdTYAQEBBAQElCmP0NBQatWqVYE7KM7X19cRsyy8vbXCYlXx9vYu0s/l7auy8JSYzowL9h/QrnpPuTKup8QszN37qqb3fwEz+wk85/7PFLO0InHfvn2n/XpVZV9VZr7z1q1b2bVrF+3atSv62h9+sM+93rjRftyvH15PP01Iw4Zliltw/x07dgTsv5wU/JJS0F7e7zdP+Z6C/6YLm8Ftq7hmzZrRoEEDPv74Y0fb8ePHWbdunaOAvuiii/Dz8ytyzr59+/j2229PW2SLiIhI9VYwpSEVyOa/ItFdpjpMS0wkNjaW4cOHExsby7TExHK99rLLLuP222//77X5+bBwIXToYC+wzzoLnn0W3nsPylhgFxYTE0NiQgIJQAj2X1CmJSbq4cdycOlIdmZmJrt27XIc7969m23bthEREcG5557LxIkTmTdvHi1atKBFixbMmzeP4OBghg4dCkB4eDijR49mypQp1KlTh4iICKZOnUrbtm0dq42IiIhIzeMoEi0WEk62uUuRaLVaSbZYik5lsVgYFBd3xvxKeu1TFgv3rVlD2I4d9pP69IFnnoHGjSuV54LkZAbFxWl1kQpyaZG9adMmevXq5TgumCc9YsQIli9fTkJCAjk5OYwfP56DBw8SExPDRx99VGQ+U0pKCr6+vgwePJicnBx69+7N8uXL8fHxqfL7EREREffhrkViRkYGUHwqS0ZGxhlzPPW1Ewr+e8cOCAuzj2aPHm1fOcQEMTExbvN18zQuLbJ79uyJYRilft7Ly4ukpKTTLhcTGBjIkiVLWLJkiRMyFBEREU/mjkVi4akshec7l2UqS8E5LwKjAP+T/w537kz4669DkybmJywV4rZzskVERETcmdVqJS0tDavVWq7XVWa+c0x0NG/37s1QwAfIBFb16UP411+rwHYzbru6iIiIiIi7mpaYSLLF4jhOTEhgQXJymV9foaksu3fDqFFc8/nnAPzRqhUH5s9n0KBB5U1fqoBGskVERETKofDDh1mABUi2WCo0oj1s2LAzF9g2GyxdCm3bwuefQ3AwuQsXctbXX9NBBbbbUpEtlZKUlISXl1eRfwVrnIuIiFRHJT24WLjdVL/8AldcAePHQ1YWdO8OO3aQN2YMaD8Nt6bekUqLiopi3759jn/ffPONq1MSERFxmipZg9sw4Mkn7aPXn30GQUGweDGsXQvnn2/edcRpVGRXMxs3bqR3796cffbZxUaYDx065JRr+vr60qBBA8e/unXrOuU6IiIi7sDpG7X89hv07QvjxkFmJlxyCezYAXffXSWj1+np6eV+oLOiD4FWZyqyq5Ht27fTs2dP2rdvzxdffMGaNWuIiIigV69evPLKKyVugzpv3jxCQ0NP++/LL7887XV//PFHIiMjadasGUOGDOHnn3920h2KiIi4hwXJyWzcuJHnn3+ejRs3Mn/BgsoHNQz7JjJt2sDHH0NgIKSk2OdhN29e+fhlMGPGDC677LJy7URZmd0rqzOtLlKN3H333QwcOJCFCxcCcOGFF3LTTTdhtVoZPHhwia8ZN25cqZ8r0PA027HGxMTw/PPP07JlS/7880/mzJlDt27d+O6776hTp07Fb0ZERMTNmboG9549cNtt8OGH9uNu3WDZMqjCbeCtVispKSnl2omyMrtXVncqsquJP//8k/Xr1/PZZ58VaQ8JCcHrNLs+RUREEBERUeHr9uvXz/Hfbdu2pWvXrpx//vmsWLHCsYOniIiIs1mtVrfb2bFMDANWrICJE+HwYQgIgLlz7cdVvHt1RXairMzuldWdpotUE5s3b8Zms9G+ffti7Z07dy71dWZMFyksJCSEtm3b8uOPP1b4XkRERMrDY6cr7N0L/fvDrbfaC+yYGNi2DaZMqfICGyr2QGeVPATqoVRkVxM2mw2AnJwcR9s333zDF198wS233FLq68aNG8e2bdtO++90Rfqpjh07xvfff88555xT8ZsREREpI7PWrK5ShgHPP2+fe/3+++DvDwsWwPr1cMEFLksrJiaGSZMmleuBTqc/BOrBNF2kmoiJiSEoKIiEhATuu+8+fvrpJ+666y7GjRtHt27dSn1dZaeLTJ06lQEDBnDuuedy4MAB5syZw5EjRxgxYkSFY4qIiJSVx01X2LcPxo6Fd96xH0dHw/LlcOGFLk2rwOzZsxkwYAB79uwp89SbCu1eWQOoyK4m6taty6uvvsqUKVNo164djRs3Zty4cUydOtWp192zZw833XQTf//9N3Xr1iU2NpaNGzfSpEkTp15XREQEik5XKHjwrnC72zAM/F59FaZNg4MHwc8PZs2Ce+4BX/cqx6Kjo+nVq1e5XmPqQ6DVhHv1qlRK//796d+/f5Ve8+WXX67S64mIiBTmmK5gsZBwss0V0xVO9+Cl14EDdFmwgJCCKSydOtlHr9u2rdIcpWqpyBYRERGP5urpCtMSE0m2WBzHiQkJLEhOts+9fvVVwsaPJ/zffzH8/PCaMQMSE+0j2VKtqcgWERERj+eq6QqlrRN9Q8+eXPTss/DGG3gDh5o1w3vFCs669NIqz1FcQ0X2GeTm5pKZmWlKrOzs7HKdX7BiiDifzWYr0s/l7auy8JSYzopbsPJNTk4OvibNP/Sk+/eUmOA5fVXT+98Z/QSec//u0v87duwAij54aQUuHDwYMjMxfH3JvPtuvujalejmzfEu9P+a9PR0du3aRfPmzYmOjnZqnq6M6ykxwV73mUVFNpCamkpqamqRtvz8fBdlIyIiIp6i+cntzgsevPwVeB0gM5P8qCiOPfkkmeedh/H110VeN2PGDFJSUhzHkyZNYvbs2VWVtlQBFdlAfHw88fHxRdqOHDlCeHg4gYGBhIaGmnq9ssbz9tYy5lXF29u7xH4xu+89KabZcfPy8gAICgpy2XvKHeJ6QkxP66ua2v/O7Cdw//t3ZszyxO3VqxeJCQl8ZbEwHGgN5Ht54XPfffg88ADB/v4cP3QI+K+vSty+PCWFG2+8sdxTXlx9/9UtZmBgoGmxVMWJiIiIVNQ//7Bgzx5WAfWB7PPOw+frr+HBB+2bzJSgpLW9C7e7M6vVSlpamntv9uMmVGSLiIiIVMTq1fZdG196Cby9Yfp0gnfuhDPslFzWrcjdraD12O3rXURFtoiIiHi8Ki1IDx6E4cNh4EDYvx9at4YNG2DePAgIOOPLy7IVubsVtB65fb2LqcgWERERj1alBel770FUFKSl2UevExJgyxbo0qVcYRYkJ7Nx40aef/55Nm7cyPwFCxyfc8eC1pOnuLiKimwRERHxWFVWkB46BLfeCv37w7590KoV/N//QXIyVPBhuZiYGIYNG1bsYUd3LGjLOsVF/qMiW0RERDxWlRSka9bY514vXw5eXjBlCmzdCrGx5l2jEHcsaMsyxUWK0hJ+IiIi4rEKF6QFy+EVbj8Tq9Va+nbshw/bC+pnnwXgSP36/D57NlG3325O8qVwFLQWCwkn29yhoHX19vWeRkW2GwsPD+eaa6457TkFu0Kavaa2M+I6K2Z2djbBwcGVihseHm5aTiIiUnUqU5BOS0wk2WJxHCcmJHD/Aw/YDz76CEaPhj17sAGLgfv+/JOcsWNJ/OknFiQnm34vhblrQeuq7es9kYpsN5aWlnbGcwq2Ajd7MXZnxHVGzEOHDrFu3Tp69OhBrVq1TIsrIiKeoyIFaeG53I4NYSwWrrr0Urq+8YZ9agiQ26gRV+zZwzXA34XOGxQX5/RiUwWtZ1ORLSIiIh6vvAVpSXO5PwQ6jhiB37//2k+66y5WtWvH+jFj+LDQeQknX19dC+D09HT27NnjViPonkgPPoqIiEiNc+rDhRnAJ0DYv/9ia9oU1q6FRx/lvLZti5znDg8hOtOMGTO47LLL3GZ9bk+mIltERERqnIK53O8DfwIdTrYfv/12sjdsgJ49i5xXE1bVsFqtpKSkuNX63J5M00VERESk5snMZEFWluPwWIMGBLz4IsdL2FTGXR9CNFtJU2iq+9QYZ1KRLSIiIjXLF1/YN5b5+Wf78dixBDz0EISFwcmH9E9VEx5CrOxyiFKUpouIiIhIzZCVBRMmQI8e9gL73HP5/tFHSbv4Yqw7d7o6O5eLiYlh0qRJNWJqTFXQSLaIiIhUf+vX20evd+2yH48Zw8zgYGbffbfjlCLrZNdQs2fPZsCAAVpdxAQqskVERKT6ysmB++6DRYvAMKBRI3jmGay1ajE7NrbYOtl9r7yS6Oho1+bsYtHR0fTq1cvVaXg8TRcRERGR6mnDBujQAVJS7AX2qFHw7bfQt2+JD/kB7CoY6RapJBXZIiIiUr3k5kJCAlxyCWRkQGQkvPcePPsshIcDxdfJLnjIr3nz5i5JWaofFdkiIiJSfVit0LEjPPQQ2GwwfLh99Pqqq4qcVtr61zV9qoiYR3OyRURExPMdOwZJSWCx2IvrBg3gqadgwIBSX1LS+teZpSzhJ1JeKrLPIDc317Q3XHZ2tilxnB3TWXGdETMnJ8fx0dfXvG9nT7l/T+p/Z/SVJ92/p8QEz+mrmt7/+vn3X0zvzZsJGDcOn//9D4ATN97IMYsFIiJKXfe6QFRUFFFRUQBkZmZ6TF/V9O9/Z91/bm6uabFUZAOpqamkpqYWacvPz3dRNiIiIlImx47h//DD+C1ciFd+Pra6dTm2eDH5pxm9FqkqKrKB+Ph44uPji7QdOXKE8PBwAgMDCQ0NNfV6ZsdzVkxnxTUzZl5eHgBBQUFun6unxTQ7rjP7yhPu35Nielpf1dT+r+k//7y3bSNg7Fh8CjaRGTIE7yVLCDr7bFPiO6OvvvvuO/bt22fq+tM19fvfWTEDAwNNi6UiW0RERDzH8eMwdy5Bc+filZ8PdevC0qVw3XWuzuy0nl+xgjdXrXIcJyYksCA52YUZibNpdRERERHxDNu3Q5cuMHs2Xvn5nBg0CL77zu0L7E2bNvHmqlVYgCzAAiRbLFitVhdnJs6kIltERETc24kTMHs2dO5sL7Tr1CF3xQqOPf+8fSTbzRVscHPqxjcFG+JI9aTpIiIiIuK+vvkGRoyArVvtx4MGwdKl5IWEuDavcijY4CaV/7Zwh/82xJHqSSPZIiIi4n7y8mDuXLjoInuBHREBL70Eb7wB9eu7Orty6dy5M3GDBhXb+Mashx/FPWkkW0RERFzOarX+tylMSAiMHAmbN9s/OXAgPPGEfYMZDzV8xAjuGD/e9NVFxH2pyBYRkRqlSDGnQsctTEtMJNliwQeYClzk44Nvfj7Urg2PPgo33wxeXq5Os9I6d+5MrVq1XJ2GVBFNFxERkRpjWmIisbGxDB8+nNjYWKYlJro6pRrParWSbLHwDHAMWAD45udz8OKL4dtv4ZZbqkWBLTWPimwREakRCoo5LaPmXn783/+YCowCfAADGAEs7t2btE8/Vf+Ix1KRLSIiNULBcmlaRs2N/PADAx9+mIcALyAfeAJ4Hpg1e7b+4iAeTUW2iIjUCAXLpaUC2dTMZdSsVitpaWmuHx3Oz4dHHoEOHQj79lty/f0Zhf1BsfEnT9FfHMTTqcgWEZEaISYmhsSEhBq7jJrbzEf/8Ufo3h2mToXcXOjTh8Bduxi7cSPPP/88M2fOBPQXB/F8KrJFRKTGWJCczMaTxdzGjRuZv2CBq1OqEm4xH91mg8WLoX17+OorCAuDp5+GNWugcWNiYmIYNmwY/fr1A0r+i4PbjMSLlIGKbBERqVEKirmaMoINrp+P7vXTTwT16wcTJ0JODvTubd/J8bbbiq0cUtpfHFa9+WaRkfgZM2ZUSe4iFaV1skVERKq5wvPRq3Rbb5sNUlMJnjYNr+xsCAmBhx+GsWNPuyzfguRkBsXFOdYzB4iNjcVSKP+ElBQGDBhAr169nHsPIhWkkWwREZFqziXz0X/+GS67DO6+G6/sbPK6d7ePXo8bV6Z1rwv/xaG0kfhdu3Y5LX2RytJItoiISA1w6uiw0wpsmw2efBLuuQeysiA4mNw5c8gbPZrQs86qUMjSRuKbN29uSsoizqAiW0REpIaIiYlx7uj1L7/A6NHw2Wf24x494LnnyKtXr1JhHSPxFgsJJ9smT55MdHR0peKKOJOKbBEREakcw4CnnrIvy5eZCUFBsGAB3HkneHvb2yrp1JH4qKgoExIXcR4V2SIiIlJxv/1mXyXk44/tx5dcAsuWgROmchQeic80oXB3FavV6vxpO+JyevBRREREys8w4NlnoU0be4EdGAgpKfD5504psKsLt9kUSJxOI9lnkJuba9pvy9nZ2abEcXZMZ8V1RsycnBzHR19f876dPeX+Pan/ndFXnnT/nhITPKevanr/u/Lnn9fevQTcdRe+J0ev82NiyF26FKNFC/s62BWI6Yw83SVuQV/93//9n2NTIMdShBYLfa+8stzzyz3p/j0lJtjrPrOoyAZSU1NJTU0t0pafn++ibERERNyUYeD74osETJuG1+HDGAEBHJ8xgxPx8eDj4+rs3N7PP/8MFF2KMAH7UoR6iLP6UZENxMfHEx8fX6TtyJEjhIeHExgYSGhoqKnXMzues2I6K66ZMfPy8gAICgpy+1w9LabZcZ3ZV55w/54U09P6qqb2f5X//PvjD7j9dnjvPftxTAxey5cTcMEFBFQ0pgk8of8L+qp169ZA8aUI27VrV+HrecL9e1LMwMBA02JpTraIiIiUzjAgLQ2iouwFtr8/JCfD//0fXHCBq7OrFKvVSlpaGlartUqu17lz56rfFEhcRiPZIiIiUrJ9++xboL/zjv24c2dYsQLr0aNkvPSSR6+OMS0xkWSLxXGcmJDAguRkp1+3yjYFEpfTSLaIiIgUZRjw0kv20et33gE/P5g3DzZsYNqKFR6/OobVanU8gJgFWIBki6XKRrQLbxkv1ZeKbBEREXHwOnCAwJtvhptvhoMH4aKLYMsWmD4d6+bNLi1OzZKRkQEUfQCxcLuIGVRki4iIiH30+pVXCI6Oxrdg9PrBB2HDBvta2FSf4rRly5aA/cHDbP57ALGgXcQMKrJFRERqugMH4IYbYMgQvP79l/x27WDTJrj/fnuxfZKzi9OqehAxJiZGDyCK0+nBRxERkZrs9dfhjjvg77/B15djCQmcmDqV0Nq1i53qKE4tFhJOtpVWnBbeOjwqKuqMaVT1g4h6AFGcTUW2iIhITfT33xAfD6++aj9u2xZWrOBEixanfVlZitNTC+ZJkyYxe/bsUmMWfhCx8E6Ig+LinFr8xsTEqLgWp9F0ERERkWrotFMvVq2yrxzy6qv2nRrvv98+PaRjxzLFPt3qGCWt3JGSkkJ6enqp8arLXG+RwlRki4iIVDPTEhNLXmbvn39g6FCIi7PPw46KAqvV/oCjv78p1y6tYN61a1epr9GDiFIdqcgWERHxAGV9KLC0NaB/eOghe1G9ciV4e8P06bB5s32JPhOVVjA3b9681NfoQUSpjjQnW0RExM2V9FDgtOnTSzy3pJHk+kCrhJOPKrZuDcuXQ5cuTsm1pIcjJ0+eTHR09GlfpwcRpbpRkS0iIuLGSnso8PIrrijx/MIjyXcBNmA4YHh74zV1KsyaBYGBTs351IK5LKuLgB5ElOpF00VERETcWHnnOMfExDDz7rs5GwgEQoEDERF4/d//QXKy0wvswnlo63CpyVRki4iIuLFyz3Fes4akN97gVsDw8mLf0KHU27MHYmOrIFsRKaAiW0RExI2V9lBg586di554+DDcdhv06wd790KLFnh9+SXnvPgiBAW5IHORmk1zskVERNxcSQ8FHjp06L8TPvoIRo+GPXvAywsmTIC5cyE42GU5i9R0KrJFREQ8QEkPBfpmZxM0cSKsWGFvOP98WLYMLr206hMUkSLcerpIXl4e999/P82aNSMoKIjzzjuP2bNnY7PZHOcYhkFSUhKRkZEEBQXRs2dPvvvuOxdmLSIi1U1Z16iuSr6ff06vCRMIKCiw77oLtm9XgS3iJty6yE5OTuaJJ57gscce4/vvv8disfDQQw+xZMkSxzkWi4WFCxfy2GOPkZ6eToMGDbjiiis4evSoCzMXEZHqotTdE13l6FG44w5CBw0i+K+/yG/SBNauhUcfhZAQ1+YmIg5uXWRv2LCBgQMHcvXVV9O0aVOuv/56+vTpw6ZNmwD7KPaiRYu47777iIuLo02bNqxYsYLs7GxeeuklF2cvIiKerrTdE102or12LbRrB088AcDPV13F0fXroWdP1+QjIqVy6znZl1xyCU888YTjQY/t27ezfv16Fi1aBMDu3bvZv38/ffr0cbwmICCAHj168NVXXzF27NgS4x47doxjx46d9tpHjhwBIDMzs+jDJZWQnZ0N2KfBmMUZMZ0V1xkxC/5iYfZfLjzl/j2p/53RV550/54SEzynr6qi/7du3QoUXaM6Adi6dSutWrWqulwzMwmaNYuAZ54BwNa4MX9bLHwTFEQnwwCT/j8FntNXnvT+95T3lLPiekpMsNd9ZnHrIjsxMZHDhw9zwQUX4OPjQ35+PnPnzuWmm24CYP/+/QDUr1+/yOvq16/Pr7/+Wmrc+fPnM2vWrDLlsH37dn788ccK3oFUlS1btrg6BSkj9ZXnUF/9VxSl8t9uiwXt69atq5Ic6nz7LR2XLCHgzz8B2H3llewcMYK8k8vyqZ88h/rK/RUU72Zw6yL7lVde4YUXXuCll14iKiqKbdu2MXHiRCIjIxkxYoTjPC8vryKvMwyjWFth06dPZ/Lkyae99pEjR2jcuDHt27cnMjKycjdyUkHHBZu4pJIzYjorrjNiHj16lC1bttCpUyfCwsJMi+sp9+9J/e+MvvKk+/eUmOA5fVUV/d+jRw/27t1LwuLFJJz8/MSJExkzZkyFY5ZZVhZBDz5IwJNPAmBr1IjsJUuo3bMnF6Off570/veU95Sz4npKTIA//vjDtFhuXWTfc889TJs2jSFDhgDQtm1bfv31V+bPn8+IESNo0KABYB/RPueccxyvO3DgQLHR7cICAgIICAgoUw6hoaHUqlWr4jdRiK+vryOmWZwR01lxnZUrQFhYmGn9BJ5z/57U/wXM7CtPun9PiVmYu/dVVfX/okWLuOmmm4qsUV3ZmGe0fj3ceisUbJ0+ZgzeDz9M6FlnFTtVP//c//1fwN3fU86K6ykx4b/pwmZw6wcfs7Oz8fYumqKPj49jCb9mzZrRoEEDPv74Y8fnjx8/zrp16+jWrVuV5ioiItVXTEwMw4YNq1CBXS7Z2TB5MnTvbi+wGzWCNWvgqaeghAJbRNyXW49kDxgwgLlz53LuuecSFRXF1q1bWbhwIaNGjQLs00QmTpzIvHnzaNGiBS1atGDevHkEBwczdOhQF2cvIiJSDhs2wMiRkJFhPx41ChYuhPBwl6YlIhXj1kX2kiVLeOCBBxg/fjwHDhwgMjKSsWPHMmPGDMc5CQkJ5OTkMH78eA4ePEhMTAwfffSRqfPTREREnCYnB2bMsBfUNhtERsLTT8NVV7k6MxGpBLcussPCwli0aJFjyb6SeHl5kZSURFJSUpXlJSIiUhFWq7Xo3G6r1T56/b//2U8YMQJSUqB2bZfmKSKV59ZFtoiISHUxY8YMUlJSAAgA1sTE0DM93T563aCBfd71gAGuTVJETOPWDz6KiIhUB+np6aSkpGABcoA9QE+r1V5g33wzfPedCmyRakYj2SIiIiYoNhWkkF27duEPTAT8gEDgT+D7CRPoeZopkSLiuTSSLSIiUknTEhOJjY1l+PDhxMbGMi0xscjnO9hsbMJeYAN8D0QBQSd3MBaR6kdFtoiISCVYrVaSLRYsQBZgAZItFqxWKxw/DjNn0jk+nrbAAeA64EJgTGKi89fdFhGX0XQRERGRSsg4ua51PBB88mMC8NfHH8PYsbB9O17AiUGD+P3227n2r79IqODOkSLiOVRki4iIVELLli0BSMVeYC8FHgCuSkqC/HyoU4fchQvJi4vjotBQLnJdqiJShTRdREREPFZ6ejppaWn2qRkuEhMTQ2JCAglADNALmA145+fDoEHw3XfkxcW5LD8RcQ0V2SIi4pFmzJjBZZddVurDhlVpwdy5/DZ2LNt8fOgEEBEBL74Ib7wB9eu7LC8RcR0V2SIi4nGsVqtj3eliDxuW4bWmjn5/+y3ExtL4ySfxyc+HgQPt614PHQpeXuZcQ0Q8jopsERHxOCU9bFi4vTRnWmqvXPLyYP58uOgi2LzZvhV6WhqsWmXfwVFEajQV2SIi4nEKP2yYffJj4faSnHapvfL6/nvo1g3uvde+TF///vYR7Vtu0ei1iAAqskVExAPFxMQwadIkEoAQ7EvmFYxKlzYVpKKj30Xk58NDD0HHjpCeDuHhsGIFrF4NkZGVuCMRqW60hJ+IiHik2bNnM2DAAPbs2UPLli1Z9eabxMbGOj6fmJDAguRkx/GpS+2VZfS7iB9+gJEjYeNG+3G/fvD009CwYaXvRUSqH41ki4iIx4qOjmbYsGEAZ5wKUnipvcKj32fcFCY/Hx55BDp0sBfYZ50Fzz4L772nAltESqWRbBER8Xil7bqYkZFRpIhekJzMoLg4MjIyaFmWXRczMuDWW+Grr+zHffrAM89A48bOuA0RqUZUZJ9Bbm4umZmZpsTKzs42JY6zYzorrjNi5uTkOD76+pr37ewp9+9J/e+MvvKk+/eUmOA5fVU4ZqNGjYDiU0EaNWpU7Gd4VFQUUVFRACX+fM/OzgabDb/UVPyTkvDKzcUIC+PYvHnkjRhhf7CxnP9f8JT3FHjO96onvf895T3lrLieEhPsdZ9ZVGQDqamppKamFmnLz893UTYiIlJe0dHR9gchU1JIONl25ZVXViiWz+7dhE2ciP/Judd5vXpxLDUVQ6PXIlIOKrKB+Ph44uPji7QdOXKE8PBwAgMDCQ0NNfV6ZsdzVkxnxTUzZl5eHgBBQUFun6unxTQ7rjP7yhPu35NielpfFcRcuHAhN954Iw8++CDvvfcea9asYc2aNcUegCyVzQapqYRMm4ZXdjaEhMAjj+B7++34mrQsX2n3b7Vayz6F5ST9/HNeTLPjetp7yllxPSFmYGCgabH04KOIiFQr7733XvnXwv75Z7jsMrj7bryys8nr3h2++QbGjnX6utembpAjIm5DRbaIiFQb5V4L22aDxx+Hdu1g3ToIDiZ34UJy33kHmjVzer6mbpAjIm5FRbaIiFQb5doJ8pdf4IorID4esrKgRw/45hvyxowB76r536MpG+SIiFtSkS0iItVGmdbCNgx48klo2xY++wyCguDRR+3/fd55VZpvRbaHFxHPoAcfRUSkWjntWti//Qa33QYff2w/vuQSWLYMmjd3Sa6OXwosFseqKGXaIEdE3J6KbBERqXZiYmKKj14/9xxMmgRHj0JgIMyfD3fdBT4+rkuUCmyQIyIeQUW2iIhUb3v2wJgxsGaN/bhbN/votRtNySj2S4GIeDzNyRYRkerJMOzFdJs29gI7IAAefhi++MKtCmwRqZ40ki0iItXPH3/YR6/ff99+HBMDy5fDBRe4NC0RqTk0ki0iItWHYUBaGkRF2Qtsf39ITob161Vgi0iVqlCR/eWXX3LLLbfQtWtX9u7dC0BaWhrr1683NTkREZEy27cPBg6E4cPh0CHo3Bm2bIGEBPDVH25FpGqVu8h+44036Nu3L0FBQWzdupVjx44BcPToUebNm2d6giIiIqdlGPDii/bR63feAT8/mDsXNmywt4mIuEC5i+w5c+bwxBNP8PTTT+Pn5+do79atG1u2bDE1ORERkdPxOnAA4uLgllvg4EHo1Ak2b4Z779XotYi4VLmL7B9++IHu3bsXaz/rrLM4dOiQGTmJiIicnmHg+8YbBEdHw1tv2UevH3wQNm607+QoIuJi5f41/5xzzmHXrl00bdq0SPv69es5r4q3oxURkRrowAEYP57AN96wH3foYF85pH17V2YlIlJEuUeyx44dy4QJE7BarXh5efHHH3/w4osvMnXqVMaPH++MHEVEROxee80+z/qNNzB8fTk+fTpYrSqwRcTtlHskOyEhgcOHD9OrVy9yc3Pp3r07AQEBTJ06lTvvvNMZOYqISE33998QHw+vvmo/btuWnKVLsbVvj7+/v2tzExEpQbmK7Pz8fNavX8+UKVO477772LlzJzabjQsvvJDQ0FBn5SgiIiawWq1kZGTQsmVLz9rC+8034Y477NNEfHzsDzXefz+248ddnZmISKnKVWT7+PjQt29fvv/+eyIiIujcubOz8hIRERNNS0wk2WJxHCcmJLAgOdmFGZXBP//AXXfBypX246goWLECLrrIfuyEIttqtbJjxw6aN29Or169TI8vIjVHuedkt23blp9//tkZuYiIiBNYrVaSLRYsQBZgAZItFqxWq4szO42337YX1StXgre3ffR68+b/CmwnmJaYSGxsLLfffjuXXXYZ0xITnXYtEan+yl1kz507l6lTp/Luu++yb98+jhw5UuSfiIi4l4yMDADigeCTHwu3u5V//4Vhw+Daa+HPP6F1a/umMnPnQkCA0y7rkb+IiIhbK/eDj1deeSUA11xzDV5eXo52wzDw8vIiPz/fvOzcQG5uLpmZmabEys7ONiWOs2M6K64zYubk5Dg++pq48YSn3L8n9b8z+sqT7t+VMRs1agRAKvYCO7VQe0k/31zVVz4ffEDA3XfjvX8/hrc3JyZM4Pi990JgIJSQp5lf0x07dgBFfxFJONkeZcKukZ7ynoLq9/3vDnH1888zYoK97jNLuXt67dq1pl3cXaSmppKamlqkrbr9siAiNVd0dDSTJk0iISWFhJNtkydPJjo62qV5ORw6REBiIn4vvQSArUULcp94AluXLlWWQvPmzYHiv4gUtIuIlFe5i+wePXo4Iw+Xio+PJz4+vkjbkSNHCA8PJzAw0PSVU5yxEouzVndx91zz8vIACAoKcvtcPS2m2XGd2VeecP+ujrlw4UJuvPHGMq0uUqV99cEHMGYM7N0LXl4weTLeDz5IcFBQxWNWQK9evUhMSCDBYnH8IjItMdH0hx895T0F1ev739Vx9fPPc2IGBgaaFqvcRfYXX3xx2s+XtOW6iIi4XkxMjPss3Xf4MEyZAs8+az9u0QKWLYOLL3ZZSguSkxkUF6fVRUTEFOUusnv27FmsrfDcbE2zEBFxP261RvZHH8Ho0bBnj330+u67Yd48CA52bV7YfxExYw62iEi5Vxc5ePBgkX8HDhxgzZo1REdH89FHHzkjRxERqYSCpemGDx9ObGys65amO3IEbr8d+va1F9jnnw/r1sGiRW5RYIuImKncI9nh4eHF2q644goCAgKYNGkSmzdvNiUxERGpvMJL0xU80JdgsTAoLq5KR7R91q4lID4efv/d3nDXXTB/PoSEVFkOIiJVqdwj2aWpW7cuP/zwg1nhRETEBK5YI9tqtZKWlmZfYzozE8aPJ+iaa/D+/Xdo1gzWroVHH1WBLSLVWrlHsgvWEi1gGAb79u1jwYIFtG/f3rTERESk8lq2bAkUX5quoN1shbdv7wm8ER5OxOHDABwfMwb/hQvBpNUA0tPT2bVrF+3atXP9PHMRkVOUu8ju0KEDXl5eGIZRpD02NpbnnnvOtMRERKRsTvdQY0xMDMNuuYWEF14osjSdGUXpqdctmJqSgr2g9wM4fJhjDRpge+YZ8nv0wN+kArtwMQ+QmJDAguRkU2KLiJih3EX27t27ixx7e3tTt25dU9cVFBGRspkxYwYpKSmO41OLzWmJiaS98ILjeNiwYcxfsKDS1y2pyI1q04ZLgbv5by7iE0D4rFkMMHGPBXeZZy4icjrlnpO9bt06GjRoQJMmTWjSpAmNGzcmMDCQ48eP8/zzzzsjRxERKUF6ejopKSlYgCzAAiRbLFitVqxWK0lJSY5itODzjrnSlVC4yC2Iu8RiodOKFXyB/X8sNuBV4A7gPJOnErpinrmISHmVu8i+9dZbOXxyfl1hR48e5dZbbzUlKRERObNdu3YBxYvNBx98kNjYWGbNmgXAfswtRk8tcu8CtgFRn34KwNNALeBGzJuaUljheebZOH+euYhIRZS7yDYMo8jmMwX27NlT4vJ+IiLiHM2bNweKF5vvvfdekVHmhcA6zCtGC17/JHACCABaAMfq1YM1a2i3cSOpzz/Pxo0bTZmacqqYmBj7FuhACJCAc4p5EZHKKPOc7I4dO+Ll5YWXlxe9e/fG1/e/l+bn57N7926uvPJKpyQpIiLFRUdHM2nSJBJSUhwPNfa/+mrefe+9IqPbCdhX+gBzitGYmBhSb76Z3i++aH+4EUhv25boL7+E8HBiTp7jTAuSk+l75ZVaXURE3FaZi+xrr70WgG3bttG3b19CCz0h7u/vT9OmTbnuuutMT1BEREo3e/ZsbrzxRscqHwDvvvdesSX7Zs6cSb9+/SpfjObkwIwZjF+5EoDs2rX57YEHiJ40qXJxKyA6Opro6Ogi/z8SEXEXZS6yZ86cCUDTpk258cYbtZqIiIibiImJKVI8JyYkkGCxFFmyLykpqfIXslph5Ej43//sx8OHE7xoERfUrl352CIi1Uy5l/AbMWKEM/IQERGTLEhOZlBcXKlrZ5dbbi4kJcFDD4HNBg0awFNPwYABpuQrIlIdlbvIzs/PJyUlhVdffZXffvuN48ePF/n8v//+a1pyIiJSMaeObldYerp99HrnTvvxLbfA4sUQEVH52CIi1Vi5VxeZNWsWCxcuZPDgwRw+fJjJkycTFxeHt7e3OX+OFBGRCrFaraasgw3gfeIEgQ8+CF272gvs+vVh1SpIS1OBLSJSBuUusl988UWefvpppk6diq+vLzfddBPPPPMMM2bMYOPGjc7IUUREzmBaYiKxsbEMHz6c2NhYpiUmVjiWz/bt9JgyhcCFCyE/H266Cb77Dk4+AC8iImdW7iJ7//79tG3bFoDQ0FDHxjT9+/fnvffeMzc7ERE5o5J2YCzY+bFcjh+HGTMI7d2bs377DdvZZ8Prr8NLL0GdOk7IXESk+ip3kd2oUSP27dsH2DdC+OijjwD79r4BAQHmZiciImdkyjbj27ZBly7w4IN45eezt1s3jm7YAFqaVUSkQspdZA8aNIhPT26dO2HCBB544AFatGjB8OHDGTVqlOkJiojI6VVqm/ETJ2DWLIiOhu3boU4dsp57jk0JCRhnn+2slEVEqr1yry6yoNAWuddffz2NGjXiq6++onnz5lxzzTWmJucOcnNzyczMNCVWdna2KXGcHdNZcZ0RMycnx/Gx8C6kleUp9+9J/e+MvvKk+3dmzKioqGI7P06ePJmoqKjT/vzy/u47AsaOxWf7dgDyrrmGYykpHAkKgq+/dvu+qun9r59/ntP/+vnnGTHBXveZpdI9HRsbS2xsrBm5uExqaiqpqalF2vLz812UjYiIXXp6Ort27aJ58+ZER0ef9tzZs2czYMCAsp2fl4dfSgr+8+fjdeIERu3aHFu4kLzrrgMvLzh61OQ7ERGpeSpUZKelpfHEE0+we/duNmzYQJMmTVi0aBHNmjVj4MCBZufodPHx8cTHxxdpO3LkCOHh4QQGBpq+Za8ztgB21rbC7p5rXl4eAEFBQW6fq6fFNDuuM/vKE+6/vDGnJSaSbLE4jhMTEliQnHzamL169aJXr16nD/zddzBiBGzebD++5hq8nnySwAYNHKd4Wl9Vx/4vC/38c15Ms+N62nvKWXE9IaaZO5qXe0720qVLmTx5MldddRWHDh1yjPjWqlWLRYsWmZaYiEhNZdpqIYXl5cGCBdCpk73Arl3bvub1W2/Zd3AUERFTlbvIXrJkCU8//TT33XcfPj4+jvbOnTvzzTffmJqciEhNZMpqIYV9/z1cfDFMn25fpq9/f/j2W/vujV5eZqQsIiKnKHeRvXv3bjp27FisPSAggKysLFOSEhGpTqxWKytXriQ9Pb1M51dqtZDC8vPhoYegY0f4+msID4fly2H1aoiMLF8sEREpl3IX2c2aNWPbtm3F2j/44AMuvPBCM3ISEak2CnZivP3227nsssvKtBNjTEwMiQkJJAAhQMLJODExMWW/8A8/wKWXQkICHDsG/fr9Nx9bo9ciIk5X7gcf77nnHuLj48nNzcUwDL7++mtWrlzJ/PnzeeaZZ5yRo4iIRyo8tzoe+4h0gsXCoLi4MxbMC5KTGRQXR0ZGBi1btix7gZ2fD48+CvfeC7m5cNZZkJICt96q4lpEpAqVu8i+9dZbycvLIyEhgezsbIYOHUrDhg1ZvHgxQ4YMcUaOIiIeqaS51Qkn28tSNMfExJRv9PrHH+3F9P/9n/24Tx945hlo3Li8qYuISCWVuci22Wx4e9tnl4wZM4YxY8bw999/Y7PZqFevntMSFBHxVIXnVheMZBduN43NBkuW2B9szMmB0FB45BEYM0aj1yIiLlLmOdl+fn4cOHDAcXzPPffg7e2tAltEpBSmzK0+A6+ffoKePWHiRHuB3bu3feWQ229XgS0i4kJlLrINwyhy/OSTT3Lo0CGz8xERqVYWJCezceNGnnrqKT777DPmL1hgTmCbDb8nniC4Wzf48ksICYGlS+Hjj6FJE3OuISIiFVbhbdVPLbpFRKRkMTExREVFmRfw559h1CgC1q2zH/fqBc8+C82amXcNERGplHIv4SciIi5is8Hjj0O7drBuHUZwMLkLF8Inn6jAFhFxM+UayZ4xYwbBwcEAHD9+nLlz5xIeHl7knIULF5qXnYiI2P3yC4weDZ99Zj/u0YPsJUswmjUDb42XiIi4mzIX2d27d+eHH35wHHfr1o2ff/65yDleeshGRMRchgFPPQVTp0JmJgQFQXIyxMdjZGe7OjsRESlFmYvszz//3IlpiIhIMb/9BrfdZn+YEeCSS2DZMmje3LV5iYjIGelvjCIi7sYw7JvItGljL7ADA+27Nq5bpwJbRMRDVHh1ERERcYI9e+yj1x9+aD/u1s0+em32BjYiIuJUKrJFxONYrVYyMjJo1KgR0dHRrk7HHIYBy5fDpElw+DAEBMDcufZNZnx8XJ2diIiUk4psEfEo0xITSbZYHMeTJk3y/FWN9u6179D4/vv245gYe8F9wQUuTUtERCrO7edk7927l1tuuYU6deoQHBxMhw4d2Lx5s+PzhmGQlJREZGQkQUFB9OzZk++++86FGYuIs1itVpItFixAFmABUlJSsFqtLs6sggwD0tLsc6/ffx/8/e0rh6xfrwJbRMTDVajI/vLLL7nlllvo2rUre/fuBSAtLY3169ebmtzBgwe5+OKL8fPz44MPPmDnzp088sgj1KpVy3GOxWJh4cKFPPbYY6Snp9OgQQOuuOIKjh49amouIuJ6GRkZAMQDwSc/Fm53FavVSlpaWvmK/X374NprYfhwOHQIOneGrVshIQF89UdGERFPV+4i+4033qBv374EBQWxdetWjh07BsDRo0eZN2+eqcklJyfTuHFjli1bRpcuXWjatCm9e/fm/PPPB+yj2IsWLeK+++4jLi6ONm3asGLFCrKzs3nppZdMzUVEXK/lyYf/UoHskx8Lt7vCtMREYmNjGT58OLGxsUxLTDz9CwwDXnwRoqJg9Wrw87PPvd6wAS68sGqSFhERpyv3cMmcOXN44oknGD58OC+//LKjvVu3bsyePdvU5FavXk3fvn254YYbWLduHQ0bNmT8+PGMGTMGgN27d7N//3769OnjeE1AQAA9evTgq6++YuzYsSXGPXbsmOOXg9IcOXIEgMzMTA4dOmTK/WSf3DgiLy/PlHjOiumsuM6IWfAXC7P/cuEp9+9J/W9GX7Vq1YoJEyaQsHgxCSfb7oyPp1WrVqa9TwHWr1/Pzz//zIUXXkjnzp1LPW/Tpk2O6Svx2Iv+BIuFy6+4otjrsrOz8f7rL/zvuw//994DIK99e7JTU7FFRdk3miknZ/W/M95XNf37313fUyXxlPv3pP73lPeUs+J6Skyw131mKXeR/cMPP9C9e/di7WeddZap/5MD+Pnnn1m6dCmTJ0/m3nvv5euvv+buu+8mICCA4cOHs3//fgDq169f5HX169fn119/LTXu/PnzmTVrVply2L59Oz/++GPFb0KqxJYtW1ydgpRRZfuqV69eNGzYkL1799KwYUNatmzJunXrTMoOnl+xgjdXrXIcxw0axPARI0o8d+3atUDR6SsJwLvvvktWVtZ/JxoGkf/3f7R78kn8jx7F5uvLD4MH82NcHMbff9vXv3ZDel95BvWT51Bfub9sE3fSLXeRfc4557Br1y6aNm1apH39+vWcd955ZuUFgM1mo3Pnzo5pKB07duS7775j6dKlDB8+3HHeqdu5G4Zx2i3ep0+fzuTJk0977SNHjtC4cWPat29PZGRkJe7iPwUdFxwcbEo8Z8V0VlxnxDx69ChbtmyhU6dOhIWFmRbXU+7fk/rfzL7q0aMHYH6emzZt4s1Vq4qOTK9axR3jx5c4oh0SEsLixYtJLXQ+QP/+/R3ne/31F0FTp+K/ejUAeW3bkv3445zTpg3nVDJfZ/W/M95XNf37393fU4V5yv17Uv97ynvKWXE9JSbAH3/8YVqschfZY8eOZcKECTz33HN4eXnxxx9/sGHDBqZOncqMGTNMSwzsBf2Fp8xRbN26NW+88QYADRo0AGD//v2cc85//7s6cOBAsdHtwgICAggICChTDqGhoUUetKwM35MPM4WGhpoSz1kxnRXXWbkChIWFmdZP4Dn370n9X8DMvjI7z3379gHFR6b37dtXYs6XX345iQkJJFgsjukr0xITufzyy+0Hr78Od9wBf/+N4evL8YQEApKSOMvPz5R8ndlP4N595ayYzorrKe8p8Jz796T+L+Du7ylnxfWUmPDfdGEzlLvITkhI4PDhw/Tq1Yvc3Fy6d+9OQEAAU6dO5c477zQtMYCLL76YH374oUhbRkYGTZo0AaBZs2Y0aNCAjz/+mI4dOwJw/Phx1q1bR3Jysqm5iEj1V/jBysIj0z/99BNWq5WYmJhir1mQnMyguDgyMjJo2bKl/Zy//4Y774RXXrGf1LYtOUuXYmvfngCTCmwREXFvFVrCb+7cufz99998/fXXbNy4kb/++osHH3zQ7NyYNGkSGzduZN68eezatYuXXnqJp556ivh4+8JdXl5eTJw4kXnz5rFq1Sq+/fZbRo4cSXBwMEOHDjU9HxGp3mJiYuwj00AIOEanZ82addqVQ2JiYhg2bJi9wF61yr5yyCuv2HdqfOAB2LQJW/v2VXUbIiLiBiq8GU1wcDCdO3fmggsu4JNPPuH77783My8AoqOjWbVqFStXrqRNmzY8+OCDLFq0iJtvvtlxTkJCAhMnTmT8yTmTe/fu5aOPPjJ1fpqI1BwLkpP57LPPmDZtGkCRjW+SLZbS18L+5x+4+WaIi4MDB+yFttUKs2fbN5kREZEapdxF9uDBg3nssccAyMnJITo6msGDB9OuXTvHXGkz9e/fn2+++Ybc3Fy+//57x/J9Bby8vEhKSmLfvn3k5uaybt062rRpY3oeIlJzREdHOx7kLtPGN6tX23dtfOkl8PaG6dNh82a46KKqSllERNxMuYvsL774gksvvRSAVatWYbPZOHToEI8++ihz5swxPUEREVdo3rw5cIaNbw4etO/YOHAg7N8PrVvDxo0wbx6U8eFqERGpnspdZB8+fJiIiAgA1qxZw3XXXUdwcDBXX3211pMWkWojOjq62PzsaYmJ/z38+O679ikhaWn20evERNiyBaKjXZi1iIi4i3KvLtK4cWM2bNhAREQEa9ascez6ePDgQQIDA01PUETEVUpcOeTQIZg4EVassJ/UqhUsXw6xsS7MVERE3E25i+yJEydy8803ExoaSpMmTejZsydgn0bStm1bs/MTEXGpmJiY/0avP/gAxoyBvXvBywumTLE/2BgU5NokRUTE7ZS7yB4/fjxdunTh999/54orrsDb2z7j5LzzztOcbBGpng4fthfUzz5rP27RApYtg4svdm1eIiLitspdZAN07ty52BbDV199tSkJiYi4lY8+gtGjYc8e++j1xIkwZw6YvJWviIhUL+UusvPz81m+fDmffvopBw4cwGazFfn8Z599ZlpyIiIuc+QITJ4MTz9tPz7/fPvo9cnVlURERE6n3EX2hAkTWL58OVdffTVt2rTBy8vLGXmJiLiMz9q1BMTHw++/2xvuugvmz4eQENcmJiIiHqPcRfbLL7/Mq6++ylVXXeWMfEREnMZqtRZdKeRUR49CQgJBTzxhP27WDJ57Dk4+4C0iIlJW5V4n29/f37FJg4iIp5iWmEhsbCzDhw8nNjaWaYmJRU9YuxbatYOTBfbx22+HHTtUYIuISIWUu8ieMmUKixcvxjAMZ+QjImI6q9VKssWCBcgCLECyxYLVaoWsLPt0kMsug19+gSZNyHn3XY4/8giEhro2cRER8Vjlni6yfv161q5dywcffEBUVBR+fn5FPv/mm2+alpyIiBkyMjIAiAeCT35MAA6tXg1Dh8LPP9tPHDcOLBby9ayJiIhUUrmL7Fq1ajFo0CBn5CIi4hQtW7YEIBV7gf0UsAjoM38+GAY0bmxfA/uKK+wvyMx0TaIiIlJtlLvIXrZsmTPyEBFxmpiYGBITEkiwWHgbWAa0AHuBfdtt8MgjcNZZrk1SRESqlQptRgPw119/8cMPP+Dl5UXLli2pW7eumXmJiJhqwcyZTPjtNxq88gpehgGNGsEzz0Dfvq5OTUREqqFyP/iYlZXFqFGjOOecc+jevTuXXnopkZGRjB49muzsbGfkKCJSORs2QMeOnPPyy/YCe9Qo+PZbFdgiIuI05R7Jnjx5MuvWreOdd97h4osvBuwPQ959991MmTKFpUuXmp6kK+Xm5pJp0vxMZ/wS4qxfbDwl15ycHMdHX98K/2GmGE+5f0/qf2f01RnzzMnBf84c/B57DC+bDds553BsyRLyC4rrUt7bntJXzup/l/SVm8R0VlxPeU+B59y/J/W/p7ynnBXXU2KCve4zS7l7+o033uD111+nZ6G1Y6+66iqCgoIYPHiwRxbZqamppKamFmnLz893UTYiYgbv9HSMkSPx/+03AE7cdBPHkpOhdm0XZyYiIjVBuYvs7Oxs6tevX6y9Xr16HjtdJD4+nvj4+CJtR44cITw8nMDAQEJNXivX7HjOiumsuGbGzMvLAyAoKMjtc/W0mGbHdWZfFYmXmwtJSdgsFrwNg33A7UBU48YsaNy44nFN4gkxq6yv3Dims+J6ynsK3P/+nRnT7Lie9p5yVlxPiBkYGGharHLPye7atSszZ84sMpyek5PDrFmz6Nq1q2mJiYiUW3o6XHQRJCfjbRh8B4QD3Sm0+YyIiEgVKPdI9uLFi7nyyitp1KgR7du3x8vLi23bthEYGMiHH37ojBxFRE7v2DGYPRuSkyE/n5zwcG46fJiXKLr5TEZGBjExMa7NVUREaoRyF9lt2rThxx9/5IUXXuB///sfhmEwZMgQbr75ZoKCgpyRo4hIqby3bYM77rCvFgJw003sHDmSt/v2dWw+U/DERcGmNCIiIs5WoUdcg4KCGDNmjNm5iIiU3fHj9pVDHn4Y8vOhbl144gmIi+MicGw+k3Dy9GmJiRrFFhGRKlOhIvuHH35gyZIlfP/993h5eXHBBRdw5513csEFF5idn4hIcdu2wYgR+O/YYT8ePBgee8xeaJ+0IDmZQXFxZGRk0LJlS6cX2FartcquJSIi7q/cDz6+/vrrtGnThs2bN9O+fXvatWvHli1baNu2La+99pozchQRsTtxAmbNguho2LEDIyKC3BUr4JVXihTYBWJiYhg2bJjTi95piYnExsYyfPhwYmNjmZaY6NTriYiI+yv3SHZCQgLTp09n9uzZRdpnzpxJYmIiN9xwg2nJiYg47NgBI0fC1q3247g4sh96CKNePZemZbVaSbZYsPDf/O8Ei4VBcXEa0RYRqcHKPZK9f/9+hg8fXqz9lltuYf/+/aYkJSLikJcHc+dC5872AjsiAlauhNdfd3mBDfYVS8BeYBesZFK4XUREaqZyF9k9e/bkyy+/LNa+fv16Lr30UlOSEhEB7CuGxMbC/ffbp4oMHAjffQdDhoCXl6uzA/5bsSQVyEYrmYiIiF25p4tcc801JCYmsnnzZmJjYwHYuHEjr732GrNmzWL16tVFzhURKbe8PHjoIUhKguPH7VuhP/oo3HxzkeI6PT2dXbt20a5dO5dNzYiJidFKJiIiUky5i+zx48cD8Pjjj/P444+X+DkALy8v8vPzK5meiNQ4338PI0bYd28E6N8fnnwSIiOLnDYtMZFki8VxnJiQwILk5KrM1KGqVzIRERH3V+7pIjabrUz/VGCLSLnk59tHrzt2tBfY4eGwYgWsXl2swC78sGEWYMH126ZX1UomIiLiGcpdZJfk0KFDZoQRkZrqhx/gkksgIcG+RXq/fva518OHlzj3Wg8bioiIuyt3kZ2cnMwrr7ziOL7hhhuIiIigYcOGbN++3dTkRKSay8+HhQuhQwfYuBHOOguefRbeew8aNiz1ZXrYUERE3F25i+wnn3ySxo0bA/Dxxx/zySefsGbNGvr168c999xjeoIiUk39+CP06AFTpkBuLvTpY19NZNSoM64c4njYEAgBEtDDhiIi4l7K/eDjvn37HEX2u+++y+DBg+nTpw9NmzbV/+BE5MxsNli8GKZPh5wcCAuDRx6B224r17J8C5KT6XvllS5fXURERKQk5S6ya9euze+//07jxo1Zs2YNc+bMAcAwDD3sKCKnFbJvH6H9+8OGDfaG3r3t00OaNKlQvOjoaKKjowkNDTUxSxERkcord5EdFxfH0KFDadGiBf/88w/9+vUDYNu2bTRv3tz0BEWkGrDZ8H/qKXrOmIHvsWMQEgIPPwxjx7rNpjIiIiJmKneRnZKSQtOmTfn999+xWCyOEaR9+/YVWSdbRASAn3+GUaMIXrcOgBOXXorfihXQrJmLE/uP1WrVGtciImKqchfZfn5+TJ06tVj7xIkTzchHpEaqlkWezQZPPGFfli8rCyM4mB233EKT+fOpFRHh6uwcStrU5v4HHnBhRiIiUh1UaJ3stLQ0LrnkEiIjI/n1118BWLRoEW+//bapyYnUBNMSE4mNjWX48OHExsYyLTHR1SlV3i+/wBVXQHw8ZGVBjx4c/b//45errgJvU5bnN0Vpm9qkF+w2aeJ1Vq5caXpcERFxX+UeyV66dCkzZsxg4sSJzJ071/GwY61atVi0aBEDBw40PUlXys3NJTMz05RY2dnZpsRxdkxnxXVGzJycHMdHX99yfzuXqqruPz093VHkxWNf7znBYqHvlVcSHR3tkjwrFdcw8F22jID77sMrMxMjKIjjs2dz4vbbycrKgl9/NbWvKnv/O3bsAIpuapMA7Ny5k6ioqMqmB8CMGTNISUlxHE+aNInZs2ebEttZ/e+M95Wn/ExxVlz9/POMmM6K6ynvKWfF9ZSYYK/7zFLuIaUlS5bw9NNPc9999+Hj4+No79y5M998841piVWl1NRULrzwwiL/ylLgiFTWrl27gOI7Fxa0exKv338ncOBAAidMwCszk/yuXcnesIET48a51eh1YQUPa5+6qc15551nSvz09HRSUlKKjJSnpKRoRFtEpAYo969Tu3fvpmPHjsXaAwIC7CNVHig+Pp74+PgibUeOHCE8PJzAwEDTlwdzxnJjzlrCzN1zzcvLAyAoKKjKc63oPOrCMdu1awfYi7uCkeyC9vLcj0v73zDsy/BNngxHj0JgIMybh8+ECYQUKq6d2VcVjderVy/7pjYWCwkn26YlJnLppZdWKm6BPXv2AMVHyvfs2UOvXr0qFbsws7+e7thXVR3TWXGry8+/6h7T7Lie9p5yVlxPiBkYGGharHIPLzVr1oxt27YVa//ggw+48MILzchJxO2ZNY/a43cu3LMH+vWDMWPsBXa3brB9O0ya5Laj16dakJzMxo0bef7559m4cSPzFywwLba2fxcRqbnKPZJ9zz33EB8fT25uLoZh8PXXX7Ny5Urmz5/PM88844wcRdxK4YflCs+jHhQXV6HieEFyMoPi4jxrdRHDgOXLYeJEOHIEAgJg7lz7caFpZJ4iJibGKV93xy9Rp4yUe0Qfi4hIpZS7yL711lvJy8sjISGB7Oxshg4dSsOGDVm8eDFDhgxxRo4ibiUjIwMoPgUgIyOjwsWTs4o8p9i7F26/Hd5/334cE2MvuC+4wKVpuauCX6J27NhB8+bNTZ0mIiIi7qtCj7iOGTOGMWPG8Pfff2Oz2ahXrx4Ae/fupWHDhqYmKOJuCk8BKDyPutpPATAMSEuDCRPg0CHw94cHH4QpUzxy9LoqxcTEmLZaiYiIeIZKTZo8++yzqVevHvv37+euu+7StupSraSnp5OWlobVai3S7vHzqCti3z4YOBBGjLAX2NHRsHWrfaMZFdgiIiLFlLnIPnToEDfffDN169YlMjKSRx99FJvNxowZMzjvvPPYuHEjzz33nDNzFakyM2bM4LLLLiv1wUZnPiznVgwDXnwRoqLgnXfAzw/mzYOvvgI96CwiIlKqMk8Xuffee/niiy8YMWIEa9asYdKkSaxZs4bc3Fw++OADevTo4cw8RaqM1Wp1rG18ugcbPWoedQV4HThgnwry1lv2hk6dYMUKaNPGpXmJiIh4gjKPZL/33nssW7aMhx9+mNWrV2MYBi1btuSzzz5TgS3VSkkPNhZur/YMA9833iA4OtpeYPv52edeb9yoAltERKSMyjyS/ccffzjWwT7vvPMIDAzktttuc1piIq5SYx9sBDhwAMaPJ/CNN+zHHTrYVw5p396VWYmIiHicMo9k22w2/Pz8HMc+Pj6EhIQ4JSkRV4qJiWHSpEk168FGgNdes8+9fuMNDF9fjk+fDlarCmwREZEKKPNItmEYjBw5koCAAAByc3MZN25csUL7zTffNDdDEReYPXs2AwYMYM+ePZ6zQUxF/f033HknvPKK/bhtW3KWLsXWvj3+/v6uzU1ERMRDlbnIHjFiRJHjW265xfRkRNxJdHR09d84ZNUqGDfOPk3ExwemT4cHHsB2/LirMxMREfFoZS6yly1b5sw8RKQq/fMP3H03vPSS/Tgqyj73unNn+7GKbBERkUqp1GY0IuKBVq+2F9UvvQTe3vbR682b/yuwRUREpNIqtK26iHiggwftW6KnpdmPW7e2j1536eLStERERKojjWSL1ATvvmsfvU5Ls49eJyTAli0qsEVERJxEI9ki1dmhQzBxon2nRoBWreyj17GxLkxKRESk+tNItkh19cEH9h0aV6wALy/7Fulbt6rAFhERqQIayRbxMFarlYyMDFq2bElUVFTxEw4fhsmT4bnn7MctWsCyZXDxxVWbqIiISA2mkWwRDzItMZHY2FiGDx9ObGwsM2bMKHrCRx/ZR6+fe84+ej1pEmzbpgJbRESkiqnIFvEQVquVZIsFC5AFWICUlBTS09PhyBG4/Xbo2xf27IHzz4d162DhQggOdnHmIiIiNY+mi5xBbm4umZmZpsTKzs42JY6zYzorrjNi5uTkOD76+pr37eyO979jxw4A4oHgkx8TgCNvvoltxAi8f/8dgOPjxnE8KQlCQuAM37vp6ens2rWL5s2bEx0dbVquJXFGX9X0739n3b+n9FVN7/+a9POvqmI6K66nvKecFddTYoK97jOLimwgNTWV1NTUIm35+fkuykakZM2bNwcgFXuB/RTwODDwsccAsDVtyrHHHyf/0kvLFG/GjBmkpKQ4jidNmsTs2bPNTVpERKSGUpENxMfHEx8fX6TtyJEjhIeHExgYSGhoqKnXMzues2I6K66ZMfPy8gAICgpy+1wrGzP45LSPBOB94DmgWcEn4+PxXrCAoDLGtlqtpKSkYMFesKcCCSkp3HjjjcTExFQ615I4s69q6ve/s2J6Wl/V1P6vST//qjqm2XE97T3lrLieEDMwMNC0WJqTLeIhMjIyCAH2AGuxF9i/AO9NngyPPQbl+EGTkZEBFJ16Urjd1axWK2lpaVitVlenIiIiUiEqskU8xEVZWWwHGp483ga0BYL79y93rJYtWwL2Eezskx8Lt7vSqSuoTEtMdHVKIiIi5aYiW8TdZWfDhAlceMcdnA/8BlwBdARunzy5yAOLZRUTE0NiQgIJQAj2KSjTEhOLTBVxhZJWUEm2WDSiLSIiHkdzskXc2fr1cOutsGuX/fi22zhw000M37uXOaVtRlNGC5KTGRQX59jYxtUFNpQ8jSXhZLs75CciIlJWKrJF3FFODtx3HyxaBIZBVkQEvz/wABdMnEhnoPPJ0yq7vGRMTIxbFa+Fp7EUPJBZuF1ERMRTqMgWcTcbNsDIkXByVPdZYPK//3Jk0iQS9+1jQXJypS9RsDX78ePH8ff3d5uRbMc0FouFhJNt7jCNRUREpLxUZIs4SUEhW+YCNicHZsyw79Jos3G8bl2u/esvegH7OLnMnsXCoLi4ShWd0xITSbZYirUnJiSYUsBXljtOYxERESkvPfgo4gTlXiHDaoVOneDhh8Fmg+HDWTV7Nh9g7jJ7JT1YCHAn7vWAYUxMDMOGDVOBLSIiHktFtojJSlshIz09vfjJx47B9OnQrRv873/QoAGsXg0rVtC0Y0fA3GX2Slsfu/0pnxcREZHKUZEtYrLSCtldBSuEFEhPt49eL1hgH72++Wb47jsYMABwzjJ7pa2Pvf2Uz4uIiEjlqMgWMVlphWzz5s3t/3HsGNx/P3TtCjt3Qr16sGoVvPACREQUibUgOZmNGzfy/PPPs3HjRuYvWFCp3Eoq3AEeQw8YioiImEkPPopQgYcUT6O0FTKio6Px3rYN7rgDvv3W/okbb7RviX722aeNZ2bxW/jBQndbXURERKS6UJEtNd6pq20kJiRw/wMPVCpmsRUyOnbk+MyZ+D30EOTnQ926sHQpXHddZdOvEHdbH1tERKS60XQRqdHK9ZBiOTlWyAgIgC5d8F+wAK/8fLjhBvvcaxcV2CIiIuJ8KrKlRivzQ4oVceIEzJoF0dGwfTtGRAS5K1bAq6/aR7JFRESk2lKRLTXaGR9SrKgdOyAmBpKSIC8P4uLITk8nLy6ucnFFRETEI6jIlhqttGXyoqOjKxbwxAmYMwc6d4atW+2rhaxcCa+/jlGvnomZi4iIiDvTg49S45W0jXdmZmb5A337LYwcCZs3248HDoQnnrBvMCMiIiI1ikeNZM+fPx8vLy8mTpzoaDMMg6SkJCIjIwkKCqJnz5589913rktSPFKltvHOy4P58+Gii+wFdu3a9jWvV61SgS0iIlJDeUyRnZ6ezlNPPUW7du2KtFssFhYuXMhjjz1Geno6DRo04IorruDo0aMuylRqlJ077Vui33svHD8O/fvbR7Rvvhm8vFydnYiIiLiIRxTZmZmZ3HzzzTz99NPUrl3b0W4YBosWLeK+++4jLi6ONm3asGLFCrKzs3nppZdcmLFUe/n58NBD9m3R09MhPBxWrIDVqyEy0tXZiYiIiIt5xJzs+Ph4rr76ai6//HLmzJnjaN+9ezf79++nT58+jraAgAB69OjBV199xdixY0uMd+zYMY4dO3baax45cgSwF/iHDh2q/E0A2dnZAOTl5ZkSz1kxnRXXGTEL/mJh9l8uTper948/Ehwfj+/JtbRPXH452YsXY0RGwuHDFYrpjDzdLa4z+sqT7t9TYoLn9FVN739X/Pyr7jGdFddT3lPOiuspMYGKPZNVCrcvsl9++WW2bNlS4uYg+/fvB6B+/fpF2uvXr8+vv/5aasz58+cza9asMl1/+/bt/Pjjj+XIWFxhy5Ytzr9Ifj7nv/surV98EZ/jxzkRHMy3o0bxW+/eZLz3Hnv37qVhw4aOZQGlZFXSV2IK9ZVnUD95DvWV+yso3s3g1kX277//zoQJE/joo48IDAws9TyvU+a+GoZRrK2w6dOnM3ny5NNe+8iRIzRu3Jj27dsTadKf/ws6Ljg42JR4zorprLhmx9y0aRPffvstJ06cYMiQIYSFhZkSF4rn6v3TT/bRa6sVgBOXXUb24sU0a9SIFUlJLF682PHaCRMmkJSUdMaYzsjTneMePXqULVu20KlTJ9P6ypPu31Niguf0VU3vf2f0E3jO/XtS/3vKe8pZcT0lJsAff/xhWiy3LrI3b97MgQMHuOiiixxt+fn5fPHFFzz22GP88MMPgH1E+5xzznGcc+DAgWKj24UFBAQQEBBQphxCQ0OpVatWxW7gFL6+vo6YZnFGTGfFNTPmtMREki0Wx/HevXtZtGhRpeMWcOQaHAxLlsD06ZCTA2FhsHAhfqNHE+7lhdVqZfHixViw7xaZCiQsXsxNN91UbKUSd/+aVkVcgLCwMLd+TzkrrqfELMzd+6qm938BM/sJPOf+Pan/C7j7e8pZcT0lJvw3XdgMbv3gY+/evfnmm2/Ytm2b41/nzp25+eab2bZtG+eddx4NGjTg448/drzm+PHjrFu3jm7durkwc3Emq9VKssWCBcgCLMDixYuxnhxlNovXTz9Bz54wcaK9wO7dG775Bm67zbFySGnbshe0i4iISM3k1iPZYWFhtGnTpkhbSEgIderUcbRPnDiRefPm0aJFC1q0aMG8efMIDg5m6NChrkhZqkBJhW3CyfYKrXN9KpsNvyefxH/GDMjOhpAQePhhGDu22LJ8hbdlLxjJLtwuIiIiNZNbF9llkZCQQE5ODuPHj+fgwYPExMTw0UcfmTo/TdyLUwvb3bth1CgCPv/cftyrFzz7LDRrVuLpjm3ZLRYSTrZNS0w0p9gXERERj+VxRfbnBcXPSV5eXiQlJZX4oJlUTyUVthMnTqxcYWuzwZNPwj33QFYWRnAwx+bMIXDCBPA+/ayqkrZlFxERkZrN44psEfivsN26dStHjx5lzJgxFQ/2yy8wejR89pn9uHt3spcswTjvvDMW2AViYmJUXIuIiIiDWz/4KHI6MTExDBkypOLTRAzDPnrdtq29wA4KgsWLYe1ae4EtIiIiUkEayZaa6bff7KuEFKxMc8klsGwZNG9u+qWsVisZGRk0atSI6Oho0+OLiIiI+9FIttQshmF/kLFNG3uBHRgIKSnw+edOKbCnJSYSGxvL8OHDueyyy5gxY4bp1xARERH3oyJbao49e+Cqq+wj2EePQteusG2bfR1sHx/TL1fSet4pKSmmr+ctIiIi7kdFtlR/hmGfCtKmDaxZAwEB8NBD8OWX0KqV0y6rjWpERERqLhXZUr3t3Qv9+8OoUXD4MMTE2Eevp051yuh1YYXX885GG9WIiIjUJCqypXoyDHj+efvo9fvvg78/JCfD+vVwwQVVkoJjPW8gBPuulJMnT9ZSfyIiIjWAVhcRpylYVaPKN2jZt8++Bfo779iPo6Nh+XK48MKqy+GkwhvVaHURERGRmkNFtjjFtMREki0Wx3FiQgL3P/CAcy9qGLByJdx5Jxw8CH5+MGuWfRdHX9d9qxdsVJOZmemyHERERKRqabqImK6kVTWSLRbS09Oddk2vAwcgLg5uvtleYHfqBFu2wPTpLi2wRUREpGZSkS2mK21VjV27dpl/McOg4ZdfEta1K7z1ln30evZs2LjRPh/bQ1mtVtLS0rTcn4iIiIdSkS2mK21VjeZmb/Zy4ADBI0fS+ZFH8P73X+jQATZtggcesBfbHqrwBjaxsbFMS0x0dUoiIiJSTiqyxXQlraoxLTHR3If+XnsNoqLwX70am48POdOmwddfQ7t25l3DBUqbaqMRbREREc+iyariFIVX1ShYXcSUB//+/hvi4+HVVwHIv/BCvrztNjrceitBHjx6XaCkqTYJJ9u19J+IiIjnUJEtTlOwqoZpVq2CcePgwAH7RjL33svRO+/k8IYNpb7EZcsIVlDhqTbxaAMbERERT6Ui+wxyc3NNW3otOzvblDjOjumsuBWO+c8/BNxzD36vvQZAfuvWHHvySWwdO5Jz9CgAOTk5+J6yisiMGTNISUlxHE+aNInZs2c7N9dKxoyKimLSpEkkpKSQcLJt8uTJREVFlfh96En9n5OT4/h4al9VlCfdv6fEBM/pq5re/87oJ/Cc+/ek/veU95Sz4npKTLDXfWb5//buPS7KOu//+HsUmRkFKfIA5CEKcV01U8ER0zQty3ZdzR6Ptdo8lB1cp1KxQDsoa60K3abuRqZZZHb07uhdZrnrIVtvudU0XTsQu5pleXPnL0XkoMD1+2NiYgQE4RpnLuf1fDx44HXN8Lk+Fx+/8/j49TvfocmWlJ2drezsbJ9zFRUVAcoG1TV/7z3Zp05Vs4ICGc2a6dT06To5a5Zkt5/x57Zv365FixYpS7/MCKctWqSRI0cG/QfCzJ07VyNHjlR+fr4SEhKCPl8AAFATTbYkt9stt9vtc66wsFBRUVFyOByKiIgw9Xpmx/NXTH/FbVDMn36Spk6VVq3yHHfrJtsLLyi8Xz+FV3taeXm5JMnpdPrE/e677yTVXNv83Xff6eqrrzY31wbIzc3Vnj17lJCQ0KDrX3311QHJ059x66qVGaxw/1aKabVahWr9/VknKfjv358xzY5rtTHlr7hWiOlwOEyLxe4iCD7vvSd17+5psJs1k9LTPR8s069fg0PUtY1gINY2V23Jd/fdd2vo0KFsyQcAQAigyUbwOHpUuv12aeRI6YcfpK5dpX/8Q1qwQDrLf1nWtY3guX7zI1vyAQAQmlguguCwbp10553SoUOSzSalpkqPPSY5nWcVpvpuIrVtI3iusSUfAAChiSYbpjurbfOOHZNmzJCee85z3KWLlJMjXXnlWV93Znq6MrOyvMfpaWlakJkZ0GaWLfkAAAhNLBeBqc7qI8HXr5d69vQ02DabNH26tHt3oxrsHTt2BOWyjGBZtgIAAM4tmmyYpsHrj48fl+65Rxo+XPr2W+myy6TNm6Unn5RatmzUtfPz8yX5LsuQflmuEUgLMjO1bds2LV++XBs2bND8BQsCnRIAAPAzmmwoNzdXq1atavKsb23rj6ufl6TmmzZ5Zq+XL/ecuP9+6bPPpEGDmnTthIQEScGxm0htXC6XbrnlFva8BgAgRNBkh7izWt5RjzNum1dUJPv06XKOHCl9840UHy9t3CgtWSK1atXk+0hKSmJZBgAACBo02SHM7O3l6lx/XFws9eypFitWeJ7odkt79khDhphxG15VyzJefPFFbdu2jWUZAAAgYNhdJISdaXu57t27Nypm9W3zftWxo5LffFMaOlSSVNmpk8qeflrO3/zGlPxr43K5mL0GAAABR5Mdwvy1vZzL5ZKrrMzzwTL//rfn5OTJKp49W4qMbFJsAAAAK2C5SAjzy/ZyJ05IU6dKgwd7GuxOnTxb9S1dSoMNAABCBjPZIc7UT0X85BPP7PXP2+nprruk//gPqXVrc5IFAACwCJpsNH0dc3Gx9Mgj0uLFkmFIHTpIK1ZI111nWo4NcVafNAkAAOBHLBdB0/z3f0u9e0uLFnka7Ntvl/75z3PeYGdkZJi2FSEAAEBT0WSjcUpKpAcflAYOlPLypLg46f33peefl6KizmkqeXl5WrJkSdB9pDoAAAhdNNk4e7m5Up8+nvXWlZXShAme2esbbghIOocOHZIUnB+pDgAAQhNNNhqutFSaOVMaMED68kspJkZas0Z64QXpwgsDltbFF18sKXg/Uh0AAIQemmw0zPbtUt++UmamZ/b6ttukffukkSMDnZkSExM1depUPlIdAAAEDXYXwZmVlUmPPSYtWCBVVEjt2knLlkmjRwc6Mx8ZGRm65ZZb2F0EAAAEBZps1KnZ7t3SH//oWW8tSTffLP31r1KbNgHNqy58pDoAAAgWNNn1KC0tVVFRkSmxiouLTYnj75g6eVK2efPkXLxYqqhQZZs2Klu8WBWjRnkeb+Tvwx+5lpSUeL+HhZn319kqtfJL/f0U1x+1stL9WyWmZJ1ahXr9ef2zTv2tMqb8FdcqMSVP32cWmmxJ2dnZys7O9jlXUVERoGwCq9mePbJPnqzme/dKkk7deKPKFi6U2rYNcGYAAADWQZMtye12y+12+5wrLCxUVFSUHA6HIiIiTL2e2fFMiXnqlDRvnvT441J5uYzoaJUtWiTH+PFqYU6KXmbef3l5uSTJ6XQG5+/VwjHNjuvPWlnh/q0U02q1CtX68/rnv5hmx7XamPJXXCvEdDgcpsWiyYa0Z480caK0a5fneMwYFT/xhIx27QKaFgAAgFWxhV8oO3XKM3OdlORpsKOjpVdfld54gwYbAACgCZjJDlX//Kdn9nrnTs/xqFHSM894PmAGAAAATcJMdqgpL5fmz/d8sMzOnZ5PanzpJentt2mwAQAATMJMdij5/HPP7PX27Z7j3/5WWr5cio0NaFoAAADnG2ayQ0FFhZSVJfXp42mwo6KklSulNWtosAEAAPyAmezz3VdfeWavt23zHN9wg2f2+uKLA5oWAADA+YyZ7PNVRYW0cKF0xRWeBrt1a+n556X33qPBBgAA8DNmsv0gNzdXeXl5SkxMlMvlOvcJ5OVJt98ubd3qOR4+XFqxQurY8dznAgAAEIKYyTbZzPR09e/fX+PHj1f//v01Mz3d9Gvk5uZq1apVys3N9X2gslJassQze711qxQZKT37rLRuHQ02AADAOUSTbaLc3FxlZmUpS9IJSVmSMrOyajbDTXB6Ez979mzPA/n50pAh0rRpUkmJNGyYtHevdOedks1m2vUBAABQP5psE+Xl5UmS3JJa/vy9+vmmqq2JX7xokb6fNUvq1UvaskVq1UpaulRav17q3NmU6wIAAODs0GSbKDExUZKULan45+/VzzfV6U38vZI2SEp86impuFi6+mrP7PXkycxeAwAABBBNtolcLpfS09KUJqmVpDR5lndUf/Pj9u3ba19P3QDVm/iTkppLGiKpwuGQsrOlv/1Nio9v8n0AAACgadhdxGQLMjN145gxte4uMnv2bC1atMh7nJ6WpgWZmQ2O7XK5tOCee9Rn2TKF/3zuXxdfrJi1a9Xq8svNugUAAAA0ETPZfuByuTRu3DifBjs3N1eLFi1q/JsiDUNavlzpL7+sYZLKw8N1YPp0tf/8cxmXXuqHuwhOde6sAgAAEERosuuxa9cuU5q6Jr0p8uBB6brrpHvukYqKpIEDFbZvny558kmpWeiU8FxsjwgAAGCG0OnQGmn06NGmNHWNelOkYUjPPSf16OHZLcThkBYtkjZtkhISGp2LFdW1PeKOHTsCnBkAAEBNNNn1mCtz9rx2uVyaPn36Gd8U6eO776QbbvDsc338uJSSIu3e7dkHu3nzRuVgZXX9T0B+fn6gUgIAAKgTTXY97pJ5e17PnTtXGzZs0Isvvqht27Zp/oIFNZ9kGFJOjmf2et06yW6XnnjCswd2166NvrbV1fU/AQkhNqMPAACsgSa7Hs/K3D2vk5OTa7wp0uvQIem3v5XuuEM6dkzq188ze/3AAyE5e11dXdsjJiUlBTgzAACAmtjCrx6zf/6SpNTUVHXv3l1FRUXavn278vPzlZCQoOTk5AbFKi4urv0Bw1DYq6/Knp4u29GjMsLDdfLhh3Xq/vulsDDPmx3PNmYT+SNuU2M+8uijuu76631+78ePH5cklZSUKCzMvL/OwXj/5yqmv+KWlJR4v5tVKyvdv1ViStapVajX3x91kqxz/1aqv1XGlL/iWiWmJJWWlpoWiyZbUnZ2trKzs33OVVRUSJJWr16to0eP+jTTp+93PX36dM2dO7dR17YdPiz7/fcr7IMPPNft00dlzzyjym7dGhXvfJecnNzgf9QAAAAECk22JLfbLbfb7XOusLBQUVFRcrlc6tSpk/d89f2u3fIsI0lbtEhjx46t+02Mp4mIiPCsvX7lFem++6SffpJatJAyMtQ8LU0tG/Gv3IiIiLP+mUDFNTNmeXm5JMnpdAZ9rlaLaXZcf9bKCvdvpZhWq1Wo1p/XP//FNDuu1caUv+JaIabD4TAtFmuyz1KT9ruu8r//K40ZI912m6fB7tNH2rlTeughz/IQAAAAWBpN9llq1H7XVQxDYW+8IXXvLr3zjqehnjtX2rZN6tnTTxkDAADgXKPJPkt17XJR71KRggI5xo2T4/bbpSNHpCuukHbskB591LNUBAAAAOcN1iY0woLMTN04Zozy8vKUmJhYf4P9n/8pTZmisB9/lBEWJtvDD3uWhoSHn5uEAQAAcE7RZDeSy+Wqv7n+8UfJ7ZZWr5YkVXTvrrJly9TyyivPQYYAAAAIFJaL+Mtbb3nWXq9e7fkgmUceUcnHH6uyV69AZwYAAAA/YybbbEeOeLble/VVz3H37tILL0hJSWf8UBkAAACcP5jJNtOaNZ6m+tVXpWbNpFmzPFvz8dHfAAAAIYWZbDP8v/8nTZ0qvfSS57hbN8/sdb9+AU0LAAAAgcFMdlO9957Uo4enwW7WTEpLkz79lAYbAAAghDGT3VhHj0rTpkkrV3qOu3b1zF737x/ApAAAABAMmMlujA8+8Mxer1wp2WzSjBnSrl002AAAAJDETPbZOXbM01A/95znuEsXKSdHYt9rAAAAVMNMdkN99JFn9vq55zyz19OmSbt302ADAACgBmay61NUJN1zj7R8uef4sss8s9eDBgU2LwAAAAQtmux6tB4+XDp0yHNw333S/PlSq1aBTQoAAABBjSa7Hs0OHZLi46Xnn5eGDAl0OgAAALAAmux6lI0bJz39tBQR4bdr5ObmKi8vT4mJiXK5XH67DgAAAM6NoH7j4/z585WcnKzIyEi1a9dOo0eP1ldffeXzHMMwlJGRobi4ODmdTg0ZMkT79u0zLYeSxx/3a4M9Mz1d/fv31/jx49W/f3/NTE/327UAAABwbgR1k71582a53W5t27ZN69evV3l5uYYPH64TJ054n5OVlaUnn3xSTz31lLZv366YmBhde+21On78eAAzb5jc3FxlZmUpS9IJSVmSMrOylJubG+DMAAAA0BRB3WSvW7dOEydOVPfu3dWrVy/l5OTo4MGD2rlzpyTPLPbixYv18MMPa8yYMerRo4dWrlyp4uJivfLKKwHOvn55eXmSJLeklj9/r34eAAAA1mSpNdnHjh2TJEVHR0uS9u/fr8OHD2v48OHe59jtdg0ePFhbt27VPffcU2ucsrIylZWVnfFahYWFkqSioiIdPXrUhOyl4uJiSVJ5ebkkKTY2VpKULU+Dnf3z82JjYxt8zdNjmsUfcf0Rs+p/LMz+nwur3L+V6u+PWlnp/q0SU7JOrUK9/rz+Waf+VhlT/oprlZiSp+8zi2WabMMwlJqaqoEDB6pHjx6SpMOHD0uS2rdv7/Pc9u3b65tvvqkz1vz58/WnP/2pQdf97LPP9PXXXzcy6/qNufFGpb39ttJ+Pr5pzBidOHFCmzdv9ts1z0effvppoFNAA1Er66BW1kCdrINaBb+q5t0Mlmmy7733Xu3Zs0effPJJjcdsNpvPsWEYNc5VN2vWLKWmpp7xeoWFherYsaN69eqluLi4xiV9mqrCtWzZ0ntu8ODB+uOUKcrPz1dCQoKSkpKaHNMM/ojrj5jHjx/Xp59+qj59+igyMtK0uFa5fyvV3x+1stL9WyWmZJ1ahXr9ef2zTv2tMqb8FdcqMSXp+++/Ny2WJZrs++67T2vWrNHHH3+sDh06eM/HxMRI8sxoVy29kKSCgoIas9vV2e122e32Bl07IiJCF1xwQeMSP01YWJg3ZnXXXHONrrnmGlNjNpU/4vorV0mKjIw0rU6Sde7fSvWvYmatrHT/VolZXbDXKtTrX4XXv+Cvf5VgH1P+imuVmNIvy4XNENRvfDQMQ/fee6/eeustbdiwQfHx8T6Px8fHKyYmRuvXr/eeO3nypDZv3qwBAwac63R95ObmatWqVewUAgAAEIKCeibb7XbrlVde0bvvvqvIyEjvGuyoqCg5nU7ZbDZNmzZN8+bNU5cuXdSlSxfNmzdPLVu21K233hqwvGempyszK8t7nJ6WpgWZmQHLBwAAAOdWUM9kL126VMeOHdOQIUMUGxvr/Xr99de9z0lLS9O0adM0ZcoUJSUl6dChQ/roo49MXZ92Ntj7GgAAAEE9k20YRr3PsdlsysjIUEZGhv8TaoDa9r5O+/l89+7dA5gZAAAAzpWgnsm2osTEREmePa+L9cve11XnAQAAcP6jyTaZy+VSelqa0iS1kmcWe2Z6ulwuV4AzAwAAwLkS1MtFrGpBZqZuHDNGeXl5SkxMpMEGAAAIMTTZfuJyuWiuAQAAQhTLRQAAAACTMZNtUbm5ucrLy1OHDh2UnJwc6HQAAABQDTPZFjQzPV39+/fX+PHjNXToUM2ePTvQKQEAAKAammyLqe3DbhYtWsSH3QAAAAQRmmyLqe3DbqqfBwAAQODRZFsMH3YDAAAQ/Giymyg3N1erVq06Z8s1avuwm9TUVLYLBAAACCLsLtIEM9PTlZmV5T1OT0vTgsxMv1+3+ofdsLsIAABA8GEmu5FqewNiZlbWOZ3RHjduHA02AABAEKLJbiTegAgAAIC60GQ3Em9ABAAAQF1oshuptjcgzkxP5w2IAAAA4I2P9SktLVVRUVGtjz3y6KO67vrrlZ+fr4SEBCUnJ9f5XEkqLi42PT9/xPRXXH/ELCkp8X4PCzPvr7NV7t9K9fdHrax0/1aJKVmnVqFef17/rFN/q4wpf8W1SkzJ0/eZhSZbUnZ2trKzs33OVVRUNOhnk5OTefMhAAAAfNBkS3K73XK73T7nCgsLFRUVJYfDoYiICFOvZ3Y8f8X0V1wzY5aXl0uSnE5n0OdqtZhmx/Vnraxw/1aKabVahWr9ef3zX0yz41ptTPkrrhViOhwO02KxJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJOFBTqBYFdaWqqioiJTYhUXF5sSx98x/RXXHzFLSkq838PCzPvrbJX7t1L9/VErK92/VWJK1qlVqNef1z/r1N8qY8pfca0SU/L0fWahyZaUnZ2t7Oxsn3MVFRUBygYAAABWR5Mtye12y+12+5wrLCxUVFSUHA6HIiIiTL2e2fH8FdNfcc2MWV5eLklyOp1Bn6vVYpod15+1ssL9Wymm1WoVqvXn9c9/Mc2Oa7Ux5a+4VojpcDhMi8WabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATBYW6ASCXWlpqYqKikyJVVxcbEocf8f0V1x/xCwpKfF+Dwsz76+zVe7fSvX3R62sdP9WiSlZp1ahXn9e/6xTf6uMKX/FtUpMydP3mYUmW1J2drays7N9zlVUVAQoGwAAAFgdTbYkt9stt9vtc66wsFBRUVFyOByKiIgw9Xpmx/NXTH/FNTNmeXm5JMnpdAZ9rlaLaXZcf9bKCvdvpZhWq1Wo1p/XP//FNDuu1caUv+JaIabD4TAtFmuyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmo8kGAAAATEaTDQAAAJiMJhsAAAAwGU02AAAAYDKabAAAAMBkNNkAAACAyWiyAQAAAJPRZAMAAAAmO2+a7Kefflrx8fFyOBzq27evtmzZEuiUAAAAEKLOiyb79ddf17Rp0/Twww9r165dGjRokEaMGKGDBw8GOjUAAACEoPOiyX7yySc1adIk3XnnnerWrZsWL16sjh07aunSpYFODQAAACEoLNAJNNXJkye1c+dOzZw50+f88OHDtXXr1lp/pqysTGVlZWeMe+zYMUnS4cOHzUlUUklJiSTJ6XQGdUx/xfVHzKKiIhUXF+v7779XYWGhaXGtcv9Wqr8/amWl+7dKTMk6tQr1+vP6Z536W2VM+SuuVWJKv/R9hmE0OZblm+wff/xRFRUVat++vc/59u3b19kgz58/X3/6058aFN/lcjU5RwAAAFjHkSNHFBUV1aQYlm+yq9hsNp9jwzBqnKsya9YspaamnjHe0aNH1blzZx08eLDJv+TqkpOTtX37dtPi+Sumv+KaHbOwsFAdO3bUt99+q9atW5sWV7LG/fsrpj/i+qtWVrl/K8W0Uq1Cuf68/lmn/lYaU/6Ka5WYx44dU6dOnRQdHd3kWJZvstu0aaPmzZvXmLUuKCioMbtdxW63y263Nyh+VFSUqQOiefPmpr8Y+iOmv+L6K9fWrVtbIlerxPRnXLNrZaX7t0rMKlaoVajXX+L1zyr1l6wxpvwV1yoxqzRr1vS3LVr+jY/h4eHq27ev1q9f73N+/fr1GjBgQICyAgAAQCiz/Ey2JKWmpmrcuHFKSkpSSkqKli9froMHD2ry5MmBTg0AAAAh6LxosseOHasjR45o7ty5+uGHH9SjRw+tXbtWnTt3DnRqAAAACEHnRZMtSVOmTNGUKVMCnUa93G63JWL6K66/cvUHq9y/lervD1a6f6vE9Bcr3b+VcvUHq9y/lervD1a6f6vENJPNMGMjwPNQYWGhoqKidOzYMb8tqkfTUSfroFbWQa2sgTpZB7WyDjNrZfk3PgIAAADBhiYbAAAAMBlNdh3sdrvmzJnT4P20ERjUyTqolXVQK2ugTtZBrazDzFqxJhsAAAAwGTPZAAAAgMlosgEAAACT0WQDAAAAJqPJBgAAAExGk12Lp59+WvHx8XI4HOrbt6+2bNkS6JRwmoyMDNlsNp+vmJiYQKcFSR9//LFGjhypuLg42Ww2vfPOOz6PG4ahjIwMxcXFyel0asiQIdq3b19gkg1h9dVp4sSJNcZY//79A5NsiJs/f76Sk5MVGRmpdu3aafTo0frqq698nsO4CryG1IlxFRyWLl2qyy+/XK1bt1br1q2VkpKiDz74wPu4WeOJJvs0r7/+uqZNm6aHH35Yu3bt0qBBgzRixAgdPHgw0KnhNN27d9cPP/zg/dq7d2+gU4KkEydOqFevXnrqqadqfTwrK0tPPvmknnrqKW3fvl0xMTG69tprdfz48XOcaWirr06SdP311/uMsbVr157DDFFl8+bNcrvd2rZtm9avX6/y8nINHz5cJ06c8D6HcRV4DamTxLgKBh06dNCCBQu0Y8cO7dixQ0OHDtWoUaO8jbRp48mAj379+hmTJ0/2OferX/3KmDlzZoAyQm3mzJlj9OrVK9BpoB6SjLffftt7XFlZacTExBgLFizwnistLTWioqKMZ555JgAZwjBq1skwDGPChAnGqFGjApIPzqygoMCQZGzevNkwDMZVsDq9TobBuApmF154obFixQpTxxMz2dWcPHlSO3fu1PDhw33ODx8+XFu3bg1QVqjL119/rbi4OMXHx+vmm2/Wv//970CnhHrs379fhw8f9hljdrtdgwcPZowFoU2bNqldu3ZKTEzUXXfdpYKCgkCnBEnHjh2TJEVHR0tiXAWr0+tUhXEVXCoqKvTaa6/pxIkTSklJMXU80WRX8+OPP6qiokLt27f3Od++fXsdPnw4QFmhNi6XSy+++KI+/PBDPfvsszp8+LAGDBigI0eOBDo1nEHVOGKMBb8RI0bo5Zdf1oYNG7Rw4UJt375dQ4cOVVlZWaBTC2mGYSg1NVUDBw5Ujx49JDGuglFtdZIYV8Fk7969ioiIkN1u1+TJk/X222/r17/+tanjKcy0bM8jNpvN59gwjBrnEFgjRozw/rlnz55KSUnRZZddppUrVyo1NTWAmaEhGGPBb+zYsd4/9+jRQ0lJSercubPef/99jRkzJoCZhbZ7771Xe/bs0SeffFLjMcZV8KirToyr4NG1a1ft3r1bR48e1ZtvvqkJEyZo8+bN3sfNGE/MZFfTpk0bNW/evMa/VAoKCmr8iwbBpVWrVurZs6e+/vrrQKeCM6jaAYYxZj2xsbHq3LkzYyyA7rvvPq1Zs0YbN25Uhw4dvOcZV8GlrjrVhnEVOOHh4UpISFBSUpLmz5+vXr16acmSJaaOJ5rsasLDw9W3b1+tX7/e5/z69es1YMCAAGWFhigrK9MXX3yh2NjYQKeCM4iPj1dMTIzPGDt58qQ2b97MGAtyR44c0bfffssYCwDDMHTvvffqrbfe0oYNGxQfH+/zOOMqONRXp9owroKHYRgqKyszdTyxXOQ0qampGjdunJKSkpSSkqLly5fr4MGDmjx5cqBTQzUPPPCARo4cqU6dOqmgoECPP/64CgsLNWHChECnFvKKioqUn5/vPd6/f792796t6OhoderUSdOmTdO8efPUpUsXdenSRfPmzVPLli116623BjDr0HOmOkVHRysjI0M33XSTYmNjdeDAAT300ENq06aNbrzxxgBmHZrcbrdeeeUVvfvuu4qMjPTOsEVFRcnpdMpmszGugkB9dSoqKmJcBYmHHnpII0aMUMeOHXX8+HG99tpr2rRpk9atW2fueDJp55PzSnZ2ttG5c2cjPDzc6NOnj8/2OwgOY8eONWJjY40WLVoYcXFxxpgxY4x9+/YFOi0YhrFx40ZDUo2vCRMmGIbh2W5szpw5RkxMjGG3242rrrrK2Lt3b2CTDkFnqlNxcbExfPhwo23btkaLFi2MTp06GRMmTDAOHjwY6LRDUm11kmTk5OR4n8O4Crz66sS4Ch533HGHt89r27atMWzYMOOjjz7yPm7WeLIZhmE09V8EAAAAAH7BmmwAAADAZDTZAAAAgMlosgEAAACT0WQDAAAAJqPJBgAAAExGkw0AAACYjCYbAAAAMBlNNgAEoYyMDF1xxRXe44kTJ2r06NEBywcAcHZosgGggSZOnCibzSabzaYWLVro0ksv1QMPPKATJ074/dpLlizRCy+80KDnHjhwQDabTbt37/ZbPpdccon3d1H11aFDB9Pi848KAFYXFugEAMBKrr/+euXk5OjUqVPasmWL7rzzTp04cUJLly6t8dxTp06pRYsWplw3KirKlDhn60z3MHfuXN11113e4+bNm5+rtBqsoqJCNptNzZoxpwTg3OJVBwDOgt1uV0xMjDp27Khbb71Vf/jDH/TOO+9I+mWJx/PPP69LL71UdrtdhmHo2LFjuvvuu9WuXTu1bt1aQ4cO1WeffeYTd8GCBWrfvr0iIyM1adIklZaW+jx++sxuZWWlMjMzlZCQILvdrk6dOunPf/6zJCk+Pl6S1Lt3b9lsNg0ZMsT7M3PnzlWHDh1kt9t1xRVXaN26dd6YVTPgq1ev1pAhQ+RwOPTSSy/V+buIjIxUTEyM96tt27aSJMMwlJWVpUsvvVROp1O9evXSG2+84f25iooKTZo0SfHx8XI6neratauWLFnifTwjI0MrV67Uu+++650l37RpkzZt2iSbzaajR496n7t7927ZbDYdOHBAkvTCCy/oggsu0Hvvvadf//rXstvt+uabb3Ty5EmlpaXp4osvVqtWreRyubRp06a6Cw0ATcRMNgA0gdPp1KlTp7zH+fn5Wr16td58803vzO5vfvMbRUdHa+3atYqKitKyZcs0bNgw5eXlKTo6WqtXr9acOXOUnZ2tQYMGadWqVfrLX/6iSy+9tM7rzpo1S88++6wWLVqkgQMH6ocfftCXX34pSfqf//kf9evXT3/729/UvXt3hYeHS/IsOVm4cKGWLVum3r176/nnn9fvfvc77du3T126dPHGTk9P18KFC5WTkyO73X7Wv5NHHnlEb731lpYuXaouXbro448/1m233aa2bdtq8ODBqqysVIcOHbR69Wq1adNGW7du1d13363Y2Fj9/ve/1wMPPKAvvvhChYWFysnJkSRFR0dr69atDbp+cXGx5s+frxUrVuiiiy5Su3btdPvtt+vAgQN67bXXFBcXp7ffflvXX3+99u7d63PvAGAaAwDQIBMmTDBGjRrlPc7NzTUuuugi4/e//71hGIYxZ84co0WLFkZBQYH3OX//+9+N1q1bG6WlpT6xLrvsMmPZsmWGYRhGSkqKMXnyZJ/HXS6X0atXr1qvXVhYaNjtduPZZ5+tNc/9+/cbkoxdu3b5nI+LizP+/Oc/+5xLTk42pkyZ4vNzixcvPvMvwjCMzp07G+Hh4UarVq28X0uWLDGKiooMh8NhbN261ef5kyZNMm655ZY6402ZMsW46aabar3fKhs3bjQkGT/99JP33K5duwxJxv79+w3DMIycnBxDkrF7927vc/Lz8w2bzWYcOnTIJ96wYcOMWbNm1XuvANAYzGQDwFl47733FBERofLycp06dUqjRo3SX//6V+/jnTt39i6bkKSdO3eqqKhIF110kU+ckpIS/etf/5IkffHFF5o8ebLP4ykpKdq4cWOtOXzxxRcqKyvTsGHDGpx3YWGhvv/+e1155ZU+56+88soaS1eSkpIaFPPBBx/UxIkTvcdt2rTR559/rtLSUl177bU+zz158qR69+7tPX7mmWe0YsUKffPNNyopKdHJkyd9dlNpivDwcF1++eXe408//VSGYSgxMdHneWVlZTXqAgBmockGgLNw9dVXa+nSpWrRooXi4uJqvCmwVatWPseVlZWKjY2tdf3vBRdc0KgcnE5no35Okmw2m8+xYRg1zp1+D3Vp06aNEhISfM5VVlZKkt5//31dfPHFPo9VLT1ZvXq1pk+froULFyolJUWRkZF64oknlJube8brVb150TAM77nqS3WqOJ1On3uqrKxU8+bNtXPnzhpvzoyIiKjvNgGgUWiyAeAstGrVqkZjeSZ9+vTR4cOHFRYWpksuuaTW53Tr1k3btm3T+PHjvee2bdtWZ8wuXbrI6XTq73//u+68884aj1etwa6oqPCea926teLi4vTJJ5/oqquu8p7funWr+vXr1+D7qU/Vmw0PHjyowYMH1/qcLVu2aMCAAZoyZYr3XNWsfvV7qJ6/JO//EPzwww+68MILJalB2xT27t1bFRUVKigo0KBBg87mdgCg0WiyAcCPrrnmGqWkpGj06NHKzMxU165d9f3332vt2rUaPXq0kpKSNHXqVE2YMEFJSUkaOHCgXn75Ze3bt6/ONz46HA6lp6crLS1N4eHhuvLKK/V///d/2rdvnyZNmqR27drJ6XRq3bp16tChgxwOh6KiovTggw9qzpw5uuyyy3TFFVcoJydHu3fv1ssvv2za/UZGRuqBBx7Q9OnTVVlZqYEDB6qwsFBbt25VRESEJkyYoISEBL344ov68MMPFR8fr1WrVmn79u3eXVEkzz7cH374ob766itddNFFioqKUkJCgjp27KiMjAw9/vjj+vrrr7Vw4cJ6c0pMTNQf/vAHjR8/XgsXLlTv3r31448/asOGDerZs6duuOEG0+4fAKqwhR8A+JHNZtPatWt11VVX6Y477lBiYqJuvvlmHThwQO3bt5ckjR07VrNnz1Z6err69u2rb775Rn/84x/PGPfRRx/VjBkzNHv2bHXr1k1jx45VQUGBJCksLEx/+ctftGzZMsXFxWnUqFGSpPvvv18zZszQjBkz1LNnT61bt05r1qwxfXeNxx57TLNnz9b8+fPVrVs3XXfddfqv//ovbxM9efJkjRkzRmPHjpXL5dKRI0d8ZrUl6a677lLXrl2VlJSktm3b6h//+IdatGihV199VV9++aV69eqlzMxMPf744w3KKScnR+PHj9eMGTPUtWtX/e53v1Nubq46duxo6r0DQBWbUX1xGwAAAIAmYyYbAAAAMBlNNgAAAGAymmwAAADAZDTZAAAAgMlosgEAAACT0WQDAAAAJqPJBgAAAExGkw0AAACYjCYbAAAAMBlNNgAAAGAymmwAAADAZDTZAAAAgMn+Pxdsb0VFajrYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = seed)                                 # set random number seed\n",
    "\n",
    "data_b1 = 3; data_b0 = 20; data_sigma = 5; n = 100          # set data model parameters\n",
    "\n",
    "x = np.random.rand(n)*30                                    # random x values\n",
    "y = data_b1*x+data_b0 + np.random.normal(loc=0.0,scale=data_sigma,size=n) # y as a linear function of x + random noise \n",
    "\n",
    "xhat = np.linspace(-10,40,100)                              # set of x values to predict and visualize the model\n",
    "linear_model = LinearRegression().fit(x.reshape(-1, 1),y)   # instantiate and train the frequentist linear regression model\n",
    "yhat = linear_model.predict(xhat.reshape(-1, 1))            # make predictions for model plotting\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.scatter(x, y,c='red',s=10,edgecolor='black')\n",
    "plt.plot(xhat,yhat,c='red'); add_grid()\n",
    "plt.xlabel(\"Predictor Feature\"); plt.ylabel(\"Response Feature\"); plt.title('Data and Linear Regression Model')\n",
    "plt.gca().add_patch(Rectangle((1.5,93.0),4.3,23,facecolor='white',edgecolor='black',linewidth=0.5))\n",
    "plt.annotate('$b_1$ = ' + str(np.round(linear_model.coef_[0],2)),[2,110])\n",
    "plt.annotate('$b_0$ = ' + str(np.round(linear_model.intercept_,2)),[2,103])\n",
    "plt.annotate('$\\sigma$ = ' + str(np.round(data_sigma,2)),[2,96])\n",
    "plt.xlim([0,30]); plt.ylim([0,120])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.5); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5222fe81",
   "metadata": {},
   "source": [
    "#### Assume the Prior Model\n",
    "\n",
    "We assume a multivariate Gaussian distribution for the slope and intercept parameters, $f_{b_1,b_0,\\sigma}(b_1,b_0,\\sigma)$ with indepedence between $b_1$, $b_0$, and $\\sigma$.\n",
    "\n",
    "* For a naive prior assume a very large standard deviation.\n",
    "We will work in log probability and log density to improve stability of the system. \n",
    "\n",
    "* We want to avoid product sums of a many values near zero as the the probabilities will disappear due to computer floating point precision. \n",
    "* In log space, we can add calculate probabilities of independent events by summing (instead of multiplication) these negative values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3750f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = np.zeros([3,2])                                     # prior distributions\n",
    "prior[0,:] = [5.0,1.0]                                      # Gaussian prior model for slope, mean and standard deviation\n",
    "prior[1,:] = [18.0,2.0]                                     # Gaussian prior model for intercept, mean and standard deviation\n",
    "prior[2,:] = [7.0,1.0]                                      # Gaussian prior model for sigma, k (shape) and phi (scale), recall mean = k x phi, var = k x phi^2 \n",
    "\n",
    "plt.subplot(131)                                            # slope prior distribution\n",
    "plt.plot(np.arange(0,10,0.01),stats.norm.logpdf(np.arange(0,10,0.01),loc=prior[0,0],scale=prior[0,1]),c='red'); ylim = plt.gca().get_ylim() \n",
    "plt.fill_between(np.arange(0,10,0.01),np.full(1000,-1.0e20),stats.norm.logpdf(np.arange(0,10,0.01),loc=prior[0,0],scale=prior[0,1]),color='black',alpha=0.05,zorder=1)\n",
    "plt.xlabel(r'$b_1$'); plt.ylabel('Natural Log Density'); plt.title(\"Gaussian Prior Distribution, $b_1$\"); add_grid(); plt.xlim([0,10]); plt.ylim(ylim)\n",
    "\n",
    "plt.subplot(132)                                            # intercept prior distribution\n",
    "plt.plot(np.arange(0,100,0.01),stats.norm.logpdf(np.arange(0,100,0.01),loc=prior[1,0],scale=prior[1,1]),c='red'); ylim = plt.gca().get_ylim() \n",
    "plt.fill_between(np.arange(0,100,0.01),np.full(10000,-1.0e20),stats.norm.logpdf(np.arange(0,100,0.01),loc=prior[1,0],scale=prior[1,1]),color='black',alpha=0.05,zorder=1)\n",
    "plt.xlabel(r'$b_0$'); plt.ylabel('Natural Log Density'); plt.title(\"Gaussian Prior Distribution, $b_1$\"); add_grid(); plt.xlim([0,100]); plt.ylim(ylim)\n",
    "\n",
    "plt.subplot(133)                                            # noise prior distribution\n",
    "plt.plot(np.arange(0,10,0.01),stats.norm.logpdf(np.arange(0,10,0.01),loc=prior[2,0],scale=prior[2,1]),c='red'); ylim = plt.gca().get_ylim() \n",
    "plt.fill_between(np.arange(0,10,0.01),np.full(1000,-1.0e20),stats.norm.logpdf(np.arange(0,10,0.01),loc=prior[2,0],scale=prior[2,1]),color='black',alpha=0.05,zorder=1)\n",
    "plt.xlabel(r'$\\sigma$'); plt.ylabel('Natural Log Density'); plt.title(\"Gamma Prior Distribution, $\\sigma$\"); add_grid(); plt.xlim([0,10]); plt.ylim(ylim)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.5); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061571e6",
   "metadata": {},
   "source": [
    "#### Bayesian Linear Regression with McMC Metropolis-Hastings \n",
    "\n",
    "The Bayesian linear regression with McMC Metropolis-Hastings workflow.\n",
    "\n",
    "1. assign an random initial set of model parameters\n",
    "2. apply a proposal rule to assign a new set of model parameters given the previous set of model paramters\n",
    "3. calculate the ratio of the likelihood of the new model parameters over the previous model parameters given the data\n",
    "4. conditionally accept the proposal based on this ratio, i.e., if proposal is more like accept it, if less likely with a probability based on the ratio.\n",
    "5. goto to 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb53b91d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accepted 1784 samples of 10000 attempts\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(seed = seed)\n",
    "step_stdev = 0.2\n",
    "\n",
    "thetas = np.random.rand(3).reshape(1,-1) # seed a random first step\n",
    "accepted = 0\n",
    "\n",
    "n = 10000                                                    # number of attempts\n",
    "\n",
    "for i in range(n):\n",
    "    theta_new = next_proposal(thetas[-1,:],step_stdev=step_stdev) # next proposal\n",
    "    \n",
    "    log_like_new = likelihood_density(x,y,theta_new)        # new and prior likelihoods, log of density\n",
    "    log_like = likelihood_density(x,y,thetas[-1,:])\n",
    "        \n",
    "    log_prior_new = prior_density(theta_new,prior)          # new and prior prior, log of density\n",
    "    log_prior = prior_density(thetas[-1,:],prior)\n",
    "    \n",
    "    likelihood_prior_proposal_ratio = np.exp((log_like_new + log_prior_new) - (log_like + log_prior)) # calculate log ratio\n",
    "\n",
    "    if likelihood_prior_proposal_ratio > np.random.rand(1): # conditionally accept by likelihood ratio\n",
    "        thetas = np.vstack((thetas,theta_new)); accepted += 1\n",
    "\n",
    "print('Accepted ' + str(accepted) + ' samples of ' + str(n) + ' attempts')\n",
    "\n",
    "df = pd.DataFrame(np.vstack([thetas[:,0],thetas[:,1],thetas[:,2]]).T, columns= ['Slope','Intercept','Sigma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706bf3ce",
   "metadata": {},
   "source": [
    "#### Visualize the Results\n",
    "\n",
    "Let's visualize the McMC Metropolis sample chain for each of the model parameters.\n",
    "\n",
    "* Note the burn-in and equalibrium chains.\n",
    "\n",
    "* Compare the slope and intercept model parameters to the frequentiest, linear regression solution, based only on the ordinary least squares solution, minimizing the L2 norm of the error vector over all sample data.\n",
    "\n",
    "* Compare the noise parameter to the noise added to the synthetic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f0332ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uQmCC",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.1\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.plot(np.arange(0,accepted+1,1),thetas[:,0],c='red',zorder=100) \n",
    "plt.xlabel('McMC Metropolis Sample'); plt.ylabel(r'$b_1$'); plt.title(\"McMC Bayesian Linear Regression Slope\"); add_grid()\n",
    "plt.plot([0,accepted],[linear_model.coef_[0],linear_model.coef_[0]],c='black',zorder=10)\n",
    "plt.annotate('Linear Regression',[30,linear_model.coef_[0]+0.05])\n",
    "plt.plot([0,accepted],[prior[0,0],prior[0,0]],c='black',zorder=10)\n",
    "plt.annotate('Prior Model [P' + str((int(alpha/2*100))) + ',P' + str((int((1.0-alpha/2)*100))) + ']',[30,prior[0,0]+0.07])\n",
    "lower = stats.norm.ppf(alpha/2.0,loc=prior[0,0],scale=prior[0,1])\n",
    "plt.plot([0,accepted],[lower,lower],c='black',ls='--',zorder=10)\n",
    "upper = stats.norm.ppf(1-alpha/2.0,loc=prior[0,0],scale=prior[0,1])\n",
    "plt.plot([0,accepted],[upper,upper],c='black',ls='--',zorder=10)\n",
    "plt.fill_between([0,accepted],[lower,lower],[upper,upper],color='black',alpha=0.05,zorder=1)\n",
    "plt.xlim([0,accepted]); plt.ylim([0,10])\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(np.arange(0,accepted+1,1),thetas[:,1],c='red',zorder=100) \n",
    "plt.xlabel('McMC Metropolis Sample'); plt.ylabel(r'$b_0$'); plt.title(\"McMC Bayesian Linear Regression Intercept\"); add_grid()\n",
    "plt.plot([0,accepted],[linear_model.intercept_,linear_model.intercept_],c='black',zorder=10)\n",
    "plt.annotate('Linear Regression',[30,linear_model.intercept_+0.25])\n",
    "plt.plot([0,accepted],[prior[1,0],prior[1,0]],c='black',zorder=10)\n",
    "plt.annotate('Prior Model [P' + str((int(alpha/2*100))) + ',P' + str((int((1.0-alpha/2)*100))) + ']',[30,prior[1,0]+0.07])\n",
    "lower = stats.norm.ppf(alpha/2.0,loc=prior[1,0],scale=prior[1,1])\n",
    "plt.plot([0,accepted],[lower,lower],c='black',ls='--',zorder=10)\n",
    "upper = stats.norm.ppf(1-alpha/2.0,loc=prior[1,0],scale=prior[1,1])\n",
    "plt.plot([0,accepted],[upper,upper],c='black',ls='--',zorder=10)\n",
    "plt.fill_between([0,accepted],[lower,lower],[upper,upper],color='black',alpha=0.05,zorder=1)\n",
    "plt.xlim([0,accepted]); plt.ylim([0,30])\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(np.arange(0,accepted+1,1),thetas[:,2],c='red',zorder=100) \n",
    "plt.xlabel('McMC Metropolis Sample'); plt.ylabel(r'$\\sigma$'); plt.title(\"McMC Bayesian Linear Regression Sigma\"); add_grid()\n",
    "plt.plot([0,accepted],[data_sigma,data_sigma],c='black',zorder=10)\n",
    "plt.annotate('Synthetic Data Noise',[30,data_sigma+0.06])\n",
    "plt.plot([0,accepted],[prior[2,0],prior[2,0]],c='black',zorder=10)\n",
    "plt.annotate('Prior Model [P' + str((int(alpha/2*100))) + ',P' + str((int((1.0-alpha/2)*100))) + ']',[30,prior[2,0]+0.07])\n",
    "lower = stats.norm.ppf(alpha/2.0,loc=prior[2,0],scale=prior[2,1])\n",
    "plt.plot([0,accepted],[lower,lower],c='black',ls='--',zorder=10)\n",
    "upper = stats.norm.ppf(1-alpha/2.0,loc=prior[2,0],scale=prior[2,1])\n",
    "plt.plot([0,accepted],[upper,upper],c='black',ls='--',zorder=10)\n",
    "plt.fill_between([0,accepted],[lower,lower],[upper,upper],color='black',alpha=0.05,zorder=1)\n",
    "plt.xlim([0,accepted]); plt.ylim([0,10])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.5); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c14dd3",
   "metadata": {},
   "source": [
    "#### Visualize the Chain in the Model Parameter Space\n",
    "\n",
    "Set the burn-in chain as the accepted sample at the end of the burn in chain and observed the McMC sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "357c2b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burn_chain = 250\n",
    "plt.scatter(thetas[:burn_chain,0],thetas[:burn_chain,1],s=5,marker = 'x',c='black',alpha=0.8,linewidth=0.3,cmap=plt.cm.inferno,zorder=10)\n",
    "plt.scatter(thetas[burn_chain:,0],thetas[burn_chain:,1],s=5,c=np.arange(burn_chain,accepted+1,1),alpha=1.0,edgecolor='black',linewidth=0.1,cmap=plt.cm.inferno,zorder=10)\n",
    "sns.kdeplot(data=df[burn_chain:],x='Slope',y='Intercept',color='grey',linewidths=1.00,alpha=0.9,levels=np.logspace(-4,-0,3),zorder=100)\n",
    "plt.plot(thetas[:burn_chain,0],thetas[:burn_chain,1],color='black',linewidth=0.1,zorder=1)\n",
    "add_grid(); plt.xlabel('Slope, $b_1$'); plt.ylabel('Intercept, $b_0$'); plt.title('McMC Metropolis Samples Bayesian Linear Regression') \n",
    "plt.xlim([0,5]); plt.ylim([0,25])\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.5); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9db45c3",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a demonstration of Bayesian Linear Regression with McMC Metropolis-Hastings Sampling.\n",
    "\n",
    "I have many other demonstrations on data analytics and machine learning, e.g. on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael J Pyrcz, Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9571d2d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "language": "python",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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